Introduction

Biosynthesis of Steroidal Alkaloids Are Coordinately Regulated and Differ among Tomatoes in the Red-Fruited Clade

Michael P. Dzakovich1, David M. Francis2, and Jessica L. Cooperstone1,3*

1 Department of Horticulture and Crop Science, The Ohio State University, 2001 Fyffe Court, Columbus, OH 43210

2 Department of Horticulture and Crop Science, The Ohio State University/Ohio Agricultural Research and Development Center, 1680 Madison Ave., Wooster, OH 44691

3 Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Court., Columbus, OH 43210 *Corresponding author ()

You can find all the code used to conduct the analyses and create visualizations found in the above paper (and also a number that are not in the paper but relevant). We hope this is helpful!

This work is publicly available as a pre-print on bioRxiv https://www.biorxiv.org/content/10.1101/2021.01.06.425594v1, and currently undergoing peer review.

Load libraries

library(tidyverse) # for wrangling and ggplotting
library(ggrepel) # for repelling text labels
library(readxl) # for reading in excel files
library(scales) # for adjusting comma formatting in axes
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(patchwork) # for multi-panel plot arranging
library(ggdendro) # for making nicer looking dendrograms for hierarchical clustering
library(here) # for directory management
library(knitr) # for knitting and kable()
library(plotly) # for interactive plots
library(corrplot) # for correlation plots
library(ggcorrplot) # for gg correlation plots

Boxplots

Creating one, faceted boxplot to show concentrations of all ten alkaloids (dehydromtomatidine, tomatidine, dehydrotomatine, alpha-tomatine, hydroxytomatine, acetoxytomatine, dehydrolycoperosid/F/G/dehydrescueloside A, lycoperoside F/G/escueloside A, escueloside B and total alkaloids) separated by tomato class (cultivated processing, cultivated cherry, wide-cross hybrid, wild cherry, S. pimpinellifolium).

Read in data

Alkaloids_raw <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "Raw Data Diversity Panel")

# what are the dimensions of this data?
dim(Alkaloids_raw)
## [1] 605  40
# what is the structure of this dataframe?
str(Alkaloids_raw)
## tibble [605 × 40] (S3: tbl_df/tbl/data.frame)
##  $ ID                                      : num [1:605] 7805 7898 7523 7724 7427 ...
##  $ Year                                    : num [1:605] 2018 2017 2018 2017 2018 ...
##  $ Environment                             : chr [1:605] "FreEarly18" "Fre17" "FreLate18" "Fre17" ...
##  $ Block                                   : num [1:605] 1 2 2 1 1 2 1 2 1 2 ...
##  $ Genotype                                : chr [1:605] "CULBPT_05_11" "CULBPT_05_11" "CULBPT_05_11" "CULBPT_05_11" ...
##  $ Plot_Source                             : chr [1:605] "2K17-7724" "2K9-8584" "2K17-7724" "2K9-8584" ...
##  $ Class                                   : chr [1:605] "Cultivated Processing" "Cultivated Processing" "Cultivated Processing" "Cultivated Processing" ...
##  $ Origin                                  : chr [1:605] "USA" "USA" "USA" "USA" ...
##  $ Provence                                : chr [1:605] "NY" "NY" "NY" "NY" ...
##  $ Blanca_Cluster1                         : chr [1:605] "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" ...
##  $ Blanca_Cluster2                         : chr [1:605] "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" ...
##  $ Passport_Species                        : chr [1:605] "SLL" "SLL" "SLL" "SLL" ...
##  $ Passport_Classification                 : chr [1:605] "SLL_processing_NY" "SLL_processing_NY" "SLL_processing_NY" "SLL_processing_NY" ...
##  $ Sim_Grouping                            : chr [1:605] "Arid" "Arid" "Arid" "Arid" ...
##  $ Latitude                                : chr [1:605] "40.712800000000001" "40.712800000000001" "40.712800000000001" "40.712800000000001" ...
##  $ Longitude                               : chr [1:605] "-74.006" "-74.006" "-74.006" "-74.006" ...
##  $ Dehydrotomatidine                       : num [1:605] 0 0 0.1357 0.0543 0.1395 ...
##  $ Tomatidine                              : num [1:605] 0 0.169 0.681 0.497 0.554 ...
##  $ Dehydrotomatine1                        : num [1:605] 5.73 0 5.07 19.42 0 ...
##  $ Dehydrotomatine2                        : num [1:605] 0.35 0 0 0 0 ...
##  $ TotalDehydrotomatine                    : num [1:605] 6.08 0 5.07 19.42 0 ...
##  $ Tomatine                                : num [1:605] 172.7 55.5 123.9 239 64.3 ...
##  $ Hydroxytomatine1                        : num [1:605] 1.079 0 0 0 0.879 ...
##  $ Hydroxytomatine2                        : num [1:605] 86.7 53.3 50.9 36 39.9 ...
##  $ Hydroxytomatine3                        : num [1:605] 17.83 13.63 6.5 8.56 7.23 ...
##  $ Hydroxytomatine4                        : num [1:605] 9.14 4.84 1.37 7.48 3.02 ...
##  $ TotalHydroxytomatine                    : num [1:605] 114.8 71.8 58.8 52.1 51 ...
##  $ Acetoxytomatine1                        : num [1:605] 18.9 3.56 3.93 3.34 0 ...
##  $ Acetoxytomatine2                        : num [1:605] 56.31 4.11 4.12 16.42 3.13 ...
##  $ Acetoxytomatine3                        : num [1:605] 1.89 0 0.623 1.057 0 ...
##  $ TotalAcetoxytomatine                    : num [1:605] 77.1 7.66 8.68 20.81 3.13 ...
##  $ DehydrolycoperosideFGdehydroesculeosideA: num [1:605] 5.13 2.91 2.19 0 0 ...
##  $ LycoperosideFGEsculeosideA1             : num [1:605] 10.28 5.55 5.1 0 4.05 ...
##  $ LycoperosideFGEsculeosideA2             : num [1:605] 337 200 259 203 300 ...
##  $ TotalLycoperosideFGEsculeosideA         : num [1:605] 347 205 264 203 304 ...
##  $ EsculeosideB1                           : num [1:605] 3.79 8.98 4.05 1.68 10.15 ...
##  $ EsculeosideB2                           : num [1:605] 0.924 1.898 0 0 0 ...
##  $ EsculeosideB3                           : num [1:605] 3.94 6.79 6.75 2.35 4.88 ...
##  $ TotalEsculeosideB                       : num [1:605] 8.66 17.67 10.8 4.03 15.03 ...
##  $ Total                                   : num [1:605] 732 361 474 539 438 ...

Wrangling

Setting Class as factor

Changing levels in Class so that plots go, left to right: Cultivated Processing, Cultivated Cherry, Wide Cross Hybrid, Wild Cherry, S. pimpinellifolium (from most cultivated, to most wild).

Alkaloids_raw$Class <- factor(Alkaloids_raw$Class, 
                              levels = c("Cultivated Processing", 
                                         "Cultivated Cherry", 
                                         "Wide Cross Hybrid",
                                         "Wild Cherry",
                                         "S. pimpinellifolium"))

I want to make one boxplot, with alkaloid concentration on the y, and tomato class on the x, that is faceted by alkaloid (i.e., one plot per alkaloid).

Create tidy data

To do this, we need to data wrangle and convert from wide to long (tidying).

# check structure
str(Alkaloids_raw)
## tibble [605 × 40] (S3: tbl_df/tbl/data.frame)
##  $ ID                                      : num [1:605] 7805 7898 7523 7724 7427 ...
##  $ Year                                    : num [1:605] 2018 2017 2018 2017 2018 ...
##  $ Environment                             : chr [1:605] "FreEarly18" "Fre17" "FreLate18" "Fre17" ...
##  $ Block                                   : num [1:605] 1 2 2 1 1 2 1 2 1 2 ...
##  $ Genotype                                : chr [1:605] "CULBPT_05_11" "CULBPT_05_11" "CULBPT_05_11" "CULBPT_05_11" ...
##  $ Plot_Source                             : chr [1:605] "2K17-7724" "2K9-8584" "2K17-7724" "2K9-8584" ...
##  $ Class                                   : Factor w/ 5 levels "Cultivated Processing",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Origin                                  : chr [1:605] "USA" "USA" "USA" "USA" ...
##  $ Provence                                : chr [1:605] "NY" "NY" "NY" "NY" ...
##  $ Blanca_Cluster1                         : chr [1:605] "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" ...
##  $ Blanca_Cluster2                         : chr [1:605] "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" ...
##  $ Passport_Species                        : chr [1:605] "SLL" "SLL" "SLL" "SLL" ...
##  $ Passport_Classification                 : chr [1:605] "SLL_processing_NY" "SLL_processing_NY" "SLL_processing_NY" "SLL_processing_NY" ...
##  $ Sim_Grouping                            : chr [1:605] "Arid" "Arid" "Arid" "Arid" ...
##  $ Latitude                                : chr [1:605] "40.712800000000001" "40.712800000000001" "40.712800000000001" "40.712800000000001" ...
##  $ Longitude                               : chr [1:605] "-74.006" "-74.006" "-74.006" "-74.006" ...
##  $ Dehydrotomatidine                       : num [1:605] 0 0 0.1357 0.0543 0.1395 ...
##  $ Tomatidine                              : num [1:605] 0 0.169 0.681 0.497 0.554 ...
##  $ Dehydrotomatine1                        : num [1:605] 5.73 0 5.07 19.42 0 ...
##  $ Dehydrotomatine2                        : num [1:605] 0.35 0 0 0 0 ...
##  $ TotalDehydrotomatine                    : num [1:605] 6.08 0 5.07 19.42 0 ...
##  $ Tomatine                                : num [1:605] 172.7 55.5 123.9 239 64.3 ...
##  $ Hydroxytomatine1                        : num [1:605] 1.079 0 0 0 0.879 ...
##  $ Hydroxytomatine2                        : num [1:605] 86.7 53.3 50.9 36 39.9 ...
##  $ Hydroxytomatine3                        : num [1:605] 17.83 13.63 6.5 8.56 7.23 ...
##  $ Hydroxytomatine4                        : num [1:605] 9.14 4.84 1.37 7.48 3.02 ...
##  $ TotalHydroxytomatine                    : num [1:605] 114.8 71.8 58.8 52.1 51 ...
##  $ Acetoxytomatine1                        : num [1:605] 18.9 3.56 3.93 3.34 0 ...
##  $ Acetoxytomatine2                        : num [1:605] 56.31 4.11 4.12 16.42 3.13 ...
##  $ Acetoxytomatine3                        : num [1:605] 1.89 0 0.623 1.057 0 ...
##  $ TotalAcetoxytomatine                    : num [1:605] 77.1 7.66 8.68 20.81 3.13 ...
##  $ DehydrolycoperosideFGdehydroesculeosideA: num [1:605] 5.13 2.91 2.19 0 0 ...
##  $ LycoperosideFGEsculeosideA1             : num [1:605] 10.28 5.55 5.1 0 4.05 ...
##  $ LycoperosideFGEsculeosideA2             : num [1:605] 337 200 259 203 300 ...
##  $ TotalLycoperosideFGEsculeosideA         : num [1:605] 347 205 264 203 304 ...
##  $ EsculeosideB1                           : num [1:605] 3.79 8.98 4.05 1.68 10.15 ...
##  $ EsculeosideB2                           : num [1:605] 0.924 1.898 0 0 0 ...
##  $ EsculeosideB3                           : num [1:605] 3.94 6.79 6.75 2.35 4.88 ...
##  $ TotalEsculeosideB                       : num [1:605] 8.66 17.67 10.8 4.03 15.03 ...
##  $ Total                                   : num [1:605] 732 361 474 539 438 ...
# select only the columns we want
Alkaloids_raw_trim <- Alkaloids_raw %>%
  select(Genotype, Class, 
         Dehydrotomatidine, Tomatidine, TotalDehydrotomatine, Tomatine, 
         TotalHydroxytomatine, TotalAcetoxytomatine, DehydrolycoperosideFGdehydroesculeosideA, 
         TotalLycoperosideFGEsculeosideA, TotalEsculeosideB, Total)

# go from wide to long
Alkaloids_tidy <- Alkaloids_raw_trim %>%
  pivot_longer(cols = Dehydrotomatidine:Total,
                names_to = "Alkaloid",
                values_to = "Concentration")

# check structure again  
str(Alkaloids_tidy)
## tibble [6,050 × 4] (S3: tbl_df/tbl/data.frame)
##  $ Genotype     : chr [1:6050] "CULBPT_05_11" "CULBPT_05_11" "CULBPT_05_11" "CULBPT_05_11" ...
##  $ Class        : Factor w/ 5 levels "Cultivated Processing",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Alkaloid     : chr [1:6050] "Dehydrotomatidine" "Tomatidine" "TotalDehydrotomatine" "Tomatine" ...
##  $ Concentration: num [1:6050] 0 0 6.08 172.66 114.78 ...

Re-level

Re-level Alkaloid so that it is a factor, and goes in pathway order.

unique(Alkaloids_tidy$Alkaloid)
##  [1] "Dehydrotomatidine"                       
##  [2] "Tomatidine"                              
##  [3] "TotalDehydrotomatine"                    
##  [4] "Tomatine"                                
##  [5] "TotalHydroxytomatine"                    
##  [6] "TotalAcetoxytomatine"                    
##  [7] "DehydrolycoperosideFGdehydroesculeosideA"
##  [8] "TotalLycoperosideFGEsculeosideA"         
##  [9] "TotalEsculeosideB"                       
## [10] "Total"
# change alkaloid factors to be in biosynthetic pathway order
Alkaloids_tidy$Alkaloid <- factor(Alkaloids_tidy$Alkaloid,
                                  levels = c("Dehydrotomatidine",
                                  "Tomatidine",
                                  "TotalDehydrotomatine",
                                  "Tomatine",
                                  "TotalHydroxytomatine",
                                  "TotalAcetoxytomatine",
                                  "DehydrolycoperosideFGdehydroesculeosideA",
                                  "TotalLycoperosideFGEsculeosideA",
                                  "TotalEsculeosideB",
                                  "Total"))

# check levels
levels(Alkaloids_tidy$Alkaloid)
##  [1] "Dehydrotomatidine"                       
##  [2] "Tomatidine"                              
##  [3] "TotalDehydrotomatine"                    
##  [4] "Tomatine"                                
##  [5] "TotalHydroxytomatine"                    
##  [6] "TotalAcetoxytomatine"                    
##  [7] "DehydrolycoperosideFGdehydroesculeosideA"
##  [8] "TotalLycoperosideFGEsculeosideA"         
##  [9] "TotalEsculeosideB"                       
## [10] "Total"

Set alkaloid and class labels

# make a list that has all the full names of the alkaloids
alkaloid_labels <- c("Dehydrotomatidine",
                     "Tomatidine",
                     "Dehydrotomatine",
                     "Alpha-Tomatine",
                     "Hydroxytomatine",
                     "Acetoxytomatine",
                     "Dehydrolycoperoside F/G/Dehydroesculeoside A",
                     "Lycoperoside F/G/Esculeoside A",
                     "Esculeoside B",
                     "Total Steroidal Alkaloids")

# tell alkaloid_labels which original label to refer to
names(alkaloid_labels) <- c("Dehydrotomatidine",
                            "Tomatidine",
                            "TotalDehydrotomatine",
                            "Tomatine",
                            "TotalHydroxytomatine",
                            "TotalAcetoxytomatine",
                            "DehydrolycoperosideFGdehydroesculeosideA",
                            "TotalLycoperosideFGEsculeosideA",
                            "TotalEsculeosideB",
                            "Total")

# enabling labeller for Class too
str(Alkaloids_tidy$Class)
##  Factor w/ 5 levels "Cultivated Processing",..: 1 1 1 1 1 1 1 1 1 1 ...
class_labels <- c("Cultivated Processing", 
                  "Cultivated Cherry",
                  "Wide Cross Hybrid", 
                  "Wild Cherry",
                  expression(italic("S. pimpinellifolium")))

class_labels
## expression("Cultivated Processing", "Cultivated Cherry", "Wide Cross Hybrid", 
##     "Wild Cherry", italic("S. pimpinellifolium"))

Plot

All same y-axis

All y-axis on the same range.

Alkaloids_tidy_mg <- Alkaloids_tidy %>%
  mutate(Concentration_mg = Concentration/1000)

Alkaloids_Faceted_Boxplot <- Alkaloids_tidy_mg %>%
  ggplot(aes(x = Class, y = Concentration_mg, fill = Class)) +
  geom_boxplot(aes(x = Class, y = Concentration_mg), alpha = 0.8, outlier.shape = NA) +
  geom_jitter(aes(x = Class, y = Concentration_mg, fill = Class), 
              shape = 21, size = 0.5, alpha = 0.6) +
  scale_y_continuous(trans = "log10", labels = scales::number_format()) + 
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  labs(title = "Steroidal Alkaloid Concentrations in Tomato",
       y = "mg/100 g fresh weight") +
  theme_classic() +
  theme(legend.text.align = 0,
        legend.position = "top",
        legend.title = element_blank(),
        title = element_text(size = 14),
        axis.title = element_text(size = 10),
        strip.text = element_text(size = 8),
        legend.text = element_text(size = 6),
        axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_text(size = 6),
        panel.grid.major.y = element_line(colour = "gray50", size = 0.1, linetype = "dashed")) +
  facet_wrap(~Alkaloid, ncol = 2, nrow = 5,
             labeller = labeller(Alkaloid = alkaloid_labels)) +
  annotation_logticks(sides = "l", size = 0.15) 

Alkaloids_Faceted_Boxplot
## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 501 rows containing non-finite values (stat_boxplot).

ggsave("Alkaloid_facet_mg_smallerdots_thinnerticks.pdf", 
       plot = Alkaloids_Faceted_Boxplot,
       dpi = 800,
       width = 6, height = 9)

Label both columns of y-axis

All y-axis on the same range, but the y-axis in the right column is now labelled.

Alkaloids_Faceted_Boxplot_bothyaxislabelled <- Alkaloids_tidy_mg %>%
  ggplot(aes(x = Class, y = Concentration_mg, fill = Class)) +
  geom_boxplot(aes(x = Class, y = Concentration_mg), alpha = 0.8, outlier.shape = NA) +
  geom_jitter(aes(x = Class, y = Concentration_mg, fill = Class), 
              shape = 21, size = 0.5, alpha = 0.6) +
  scale_y_continuous(trans = "log10", labels = scales::number_format(),
                     limits = c(0.00001, 100)) + 
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  labs(title = "Steroidal Alkaloid Concentrations in Tomato",
       y = "mg/100 g fresh weight") +
  theme_classic() +
  theme(legend.text.align = 0,
        legend.position = "top",
        legend.title = element_blank(),
        title = element_text(size = 14),
        axis.title = element_text(size = 10),
        strip.text = element_text(size = 8),
        legend.text = element_text(size = 6),
        axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_text(size = 6),
        panel.grid.major.y = element_line(colour = "gray50", size = 0.1, linetype = "dashed")) +
  facet_wrap(~Alkaloid, ncol = 2, nrow = 5,
             labeller = labeller(Alkaloid = alkaloid_labels),
             scales = "free_y") +
  annotation_logticks(sides = "l", size = 0.15) 

Alkaloids_Faceted_Boxplot_bothyaxislabelled
## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 506 rows containing non-finite values (stat_boxplot).
## Warning: Removed 5 rows containing missing values (geom_point).

ggsave("Alkaloid_facet_mg_smallerdots_thinnerticks_all_y_labelled.pdf", 
       plot = Alkaloids_Faceted_Boxplot_bothyaxislabelled,
       dpi = 800,
       width = 6, height = 9)

Free y-axis per alkaloid

Free_y axis (meaning all the y-axis are independent). This is the plot we used in the paper.

Alkaloids_Faceted_Boxplot_free_y <- Alkaloids_tidy_mg %>%
  ggplot(aes(x = Class, y = Concentration_mg, fill = Class)) +
  geom_boxplot(aes(x = Class, y = Concentration_mg), alpha = 0.8, outlier.shape = NA) +
  geom_jitter(aes(x = Class, y = Concentration_mg, fill = Class), 
              shape = 21, size = 0.5, alpha = 0.6) +
  scale_y_continuous(trans = "log10", labels = scales::number_format()) + 
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  labs(title = "Steroidal Alkaloid Concentrations in Tomato",
       y = "mg/100 g fresh weight") +
  theme_classic() +
  theme(legend.text.align = 0,
        legend.position = "top",
        legend.title = element_blank(),
        title = element_text(size = 14),
        axis.title = element_text(size = 10),
        strip.text = element_text(size = 8),
        legend.text = element_text(size = 6),
        axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_text(size = 6),
        panel.grid.major.y = element_line(colour = "gray50", size = 0.1, linetype = "dashed")) +
  facet_wrap(~Alkaloid, ncol = 2, nrow = 5,
             labeller = labeller(Alkaloid = alkaloid_labels),
             scales = "free_y") +
  annotation_logticks(sides = "l", size = 0.15) 

Alkaloids_Faceted_Boxplot_free_y
## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 501 rows containing non-finite values (stat_boxplot).

ggsave("Alkaloid_facet_mg_smallerdots_thinnerticks_free_y.pdf", 
       plot = Alkaloids_Faceted_Boxplot_free_y,
       dpi = 800,
       width = 6, height = 9)

PCA

Read in data

Alkaloids_LSmeans <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "LSMeans Diversity Panel")

str(Alkaloids_LSmeans)
## tibble [107 × 37] (S3: tbl_df/tbl/data.frame)
##  $ Genotype                                : chr [1:107] "CULBPT_05_11" "CULBPT_05_15" "CULBPT_05_22" "CULBPT04_1" ...
##  $ Plot_Source                             : chr [1:107] "2K9-8584" "2K9-8622" "2K17-7708-1" "2K9-8566" ...
##  $ Species                                 : chr [1:107] "Processing" "Processing" "Processing" "Processing" ...
##  $ Class                                   : chr [1:107] "Cultivated Processing" "Cultivated Processing" "Cultivated Processing" "Cultivated Processing" ...
##  $ Origin                                  : chr [1:107] "USA" "USA" "USA" "USA" ...
##  $ Provence                                : chr [1:107] "NY" "NY" "NY" "NY" ...
##  $ Blanca_Cluster1                         : chr [1:107] "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" ...
##  $ Blanca_Cluster2                         : chr [1:107] "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" "SLL_processing_2" ...
##  $ Passport_Species                        : chr [1:107] "SLL" "SLL" "SLL" "SLL" ...
##  $ Passport_Classification                 : chr [1:107] "SLL_processing_NY" "SLL_processing_NY" "SLL_processing_NY" "SLL_processing_NY" ...
##  $ Sim_Grouping                            : chr [1:107] "Arid" "Arid" "Arid" "Arid" ...
##  $ Dehydrotomatidine                       : num [1:107] 1.50e-04 -2.72e-05 -6.49e-05 -2.72e-05 -2.72e-05 ...
##  $ Tomatidine                              : num [1:107] 4.35e-03 -1.72e-04 -2.52e-04 1.26e-04 1.59e-05 ...
##  $ DehydrotomatineA1                       : num [1:107] 0.00722 0.00865 0.00852 -0.00387 0.00995 ...
##  $ Dehydrotomatine2                        : num [1:107] -1.07e-03 3.13e-17 -8.74e-16 -1.32e-15 6.58e-16 ...
##  $ TotalDehydrotomatine                    : num [1:107] 0.00615 0.00865 0.00852 -0.00387 0.00995 ...
##  $ Tomatine                                : num [1:107] 0.19291 0.28249 0.17552 -0.00614 0.29296 ...
##  $ Hydroxytomatine1                        : num [1:107] 1.03e-02 6.91e-04 -1.80e-04 1.59e-03 3.34e-15 ...
##  $ Hydroxytomatine2                        : num [1:107] 0.1597 0.1119 0.0506 0.1059 0.0227 ...
##  $ Hydroxytomatine3                        : num [1:107] 0.03527 0.02439 0.00409 0.01958 0.00392 ...
##  $ Hydroxytomatine4                        : num [1:107] 0.0864 0.01893 0.00232 0.00836 0.00883 ...
##  $ TotalHydroxytomatine                    : num [1:107] 0.2918 0.1559 0.0576 0.1355 0.0355 ...
##  $ Acetoxytomatine1                        : num [1:107] -0.068051 0.003823 0.007824 0.009859 0.000236 ...
##  $ Acetoxytomatine2                        : num [1:107] -0.12424 0.04674 0.00696 0.00953 0.03954 ...
##  $ Acetoxytomatine3                        : num [1:107] 0.011748 0.002474 -0.000115 0.000794 0.004179 ...
##  $ TotalAcetoxytomatine                    : num [1:107] -0.1805 0.053 0.0147 0.0202 0.044 ...
##  $ DehydrolycoperosideFGdehydroesculeosideA: num [1:107] -0.03699 0.00663 0.00315 0.00494 0.00274 ...
##  $ LycoperosideFGEsculeosideA1             : num [1:107] 0.00566 0.01253 0.01507 0.01004 0.01332 ...
##  $ LycoperosideFGEsculeosideA2             : num [1:107] -0.169 0.342 0.384 0.347 0.302 ...
##  $ TotalLycoperosideFGEsculeosideA         : num [1:107] -0.164 0.354 0.399 0.357 0.315 ...
##  $ EsculeosideB1                           : num [1:107] -0.034068 0.013843 0.000713 0.010083 0.008089 ...
##  $ EsculeosideB2                           : num [1:107] -0.006171 0.002183 0.000823 0.000643 -0.000357 ...
##  $ EsculeosideB3                           : num [1:107] -0.03715 0.01393 0.00281 0.0089 0.01 ...
##  $ TotalEsculeosideB                       : num [1:107] -0.07739 0.02996 0.00435 0.01963 0.01774 ...
##  $ Total                                   : num [1:107] 0.0366 0.8908 0.6618 0.527 0.718 ...
##  $ Latitude                                : chr [1:107] "40.712800000000001" "40.712800000000001" "40.712800000000001" "40.712800000000001" ...
##  $ Longitude                               : chr [1:107] "-74.006" "-74.006" "-74.006" "-74.006" ...

Prep data for PCA

Select only the data we will use for our PCA. This includes the alkaloid totals (vs each individual isomer, which is listed), and removing some of the meta-data that we don’t need to complicate our lives right now.

Alkaloids_LSmeans_forPCA <- Alkaloids_LSmeans %>%
  select(Genotype, Class,
         Dehydrotomatidine, Tomatidine,
         TotalDehydrotomatine, Tomatine,
         TotalHydroxytomatine, TotalAcetoxytomatine,
         DehydrolycoperosideFGdehydroesculeosideA, TotalLycoperosideFGEsculeosideA,
         TotalEsculeosideB, Total)

Run PCA

We are running PCA using the package FactoMineR, and doing a quick visualization with fviz_pca from FactoExtra.

quali.sup gives the indexes of the qualitative/categorical variables, in this case, Genotype and Class, which aren’t inputs to the PCA. scale.unit is set to TRUE because we want to scale data to unit variance. scale.unit = TRUE is the default.

# run PCA
Alkaloids_PCA <- PCA(Alkaloids_LSmeans_forPCA, 
                     quali.sup = 1:2, 
                     graph = FALSE,
                     scale.unit = TRUE)

# visualize the PCA and loadings plot really quickly
fviz_pca(Alkaloids_PCA)

# visualize the PCA scores plot quickly
fviz_pca_ind(Alkaloids_PCA)

This PCA scores plot isn’t too useful for us because we don’t know what point is what (they are just ordered as in our df) but we can fix this in a minute.

# visualize the PCA loadings plot quickly
fviz_pca_var(Alkaloids_PCA)

# get a scree plot
fviz_eig(Alkaloids_PCA)

Get scores and loadings coordinates

These default plots would benefit from plotting with ggplot2 so that:

  • we can see how things like Class affect scores plot locations
  • so we can make these plots prettier

Get coordinates for scores plot (where each point is an individual, i.e., a tomato accession). Then bind back to relevant meta-data.

# get scores coordinates
scores_coordinates <- as.data.frame(Alkaloids_PCA$ind$coord)

# check structure
str(scores_coordinates)
## 'data.frame':    107 obs. of  5 variables:
##  $ Dim.1: num  -1.52 -1.51 -1.56 -1.55 -1.53 ...
##  $ Dim.2: num  -1.175 -0.919 -0.947 -0.91 -0.944 ...
##  $ Dim.3: num  -0.00635 -0.06525 -0.06608 -0.08204 -0.04377 ...
##  $ Dim.4: num  0.3222 -0.0875 -0.1571 -0.0705 -0.0891 ...
##  $ Dim.5: num  0.08121 0.03031 0.03351 0.05099 0.00653 ...
# bind back to meta-data
Alkaloids_LSmeans_withPC <- bind_cols(Alkaloids_LSmeans, scores_coordinates)

Now the first 5 dimensions (Dim.1 through Dim.5) are at the end of our df.

Get coordinates for loadings plot (where each point is a variable, i.e., a alkaloid). Then bind back to indicate which alkaloid is which set of coordinates.

loadings_coordinates <- as.data.frame(Alkaloids_PCA$var$coord)

str(loadings_coordinates)
## 'data.frame':    10 obs. of  5 variables:
##  $ Dim.1: num  0.291 0.554 0.758 0.586 0.558 ...
##  $ Dim.2: num  0.0929 -0.2268 -0.5233 -0.5008 -0.1302 ...
##  $ Dim.3: num  0.082 0.538 0.269 0.528 -0.52 ...
##  $ Dim.4: num  0.92 0.13 -0.164 -0.135 0.151 ...
##  $ Dim.5: num  -0.214 0.375 -0.132 -0.127 0.548 ...
loadings_coordinates$Alkaloid <- c("Dehydrotomatidine",
                                  "Tomatidine",
                                  "TotalDehydrotomatine",
                                  "Tomatine",
                                  "TotalHydroxytomatine",
                                  "TotalAcetoxytomatine",
                                  "DehydrolycoperosideFGdehydroesculeosideA",
                                  "TotalLycoperosideFGEsculeosideA",
                                  "TotalEsculeosideB",
                                  "Total")

str(loadings_coordinates)
## 'data.frame':    10 obs. of  6 variables:
##  $ Dim.1   : num  0.291 0.554 0.758 0.586 0.558 ...
##  $ Dim.2   : num  0.0929 -0.2268 -0.5233 -0.5008 -0.1302 ...
##  $ Dim.3   : num  0.082 0.538 0.269 0.528 -0.52 ...
##  $ Dim.4   : num  0.92 0.13 -0.164 -0.135 0.151 ...
##  $ Dim.5   : num  -0.214 0.375 -0.132 -0.127 0.548 ...
##  $ Alkaloid: chr  "Dehydrotomatidine" "Tomatidine" "TotalDehydrotomatine" "Tomatine" ...

Set Class as a factor with levels going from more cultivated, to more wild, in the order specified below.

Alkaloids_LSmeans_withPC$Class <- factor(Alkaloids_LSmeans_withPC$Class, 
                                         levels = c("Cultivated Processing", 
                                                    "Cultivated Cherry", 
                                                    "Wide Cross Hybrid",
                                                    "Wild Cherry",
                                                    "S. pimpinellifolium"))

Plot

PCA Scores Plot

Alkaloids_PCA_scores <- Alkaloids_LSmeans_withPC %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_point(size = 2.5, color = "black", alpha = 0.8) +
  scale_shape_manual(values= c(24, 22, 23, 22, 21),
                     labels = class_labels) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  xlim(-2, 6) +
  ylim(-5, 6) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(x = "PC1: 32.2%",
       y = "PC2: 30.0%")

Alkaloids_PCA_scores

ggsave(filename = "PCA_scores.png",
       plot = Alkaloids_PCA_scores,
       dpi = 1200,
       width = 9,
       height = 6)

PCA Loadings Plot

Alkaloids_PCA_loadings <- loadings_coordinates %>%
  ggplot(aes(x = Dim.1, y = Dim.2, label = Alkaloid)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_point(size = 2.5, color = "black", alpha = 0.8) +
  geom_text_repel(aes(label = alkaloid_labels)) +
  xlim(-0.33,1.1) +
  ylim(-0.8, 1) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(x = "PC1: 32.2%",
       y = "PC2: 30.0%")

Alkaloids_PCA_loadings

ggsave(filename = "PCA_loadings.png",
       plot = Alkaloids_PCA_loadings,
       dpi = 1200,
       width = 9,
       height = 6)

Make one plot of the PCA scores and loadings with the package patchwork

scores_and_loadings <- Alkaloids_PCA_scores + Alkaloids_PCA_loadings

scores_and_loadings + plot_annotation(tag_levels = "A")

scores_and_loadings <- scores_and_loadings + plot_annotation(tag_levels = "A")
ggsave(filename = "PCA_scores_and_loadings.png",
       plot = scores_and_loadings,
       dpi = 1200,
       width = 14,
       height = 6)

Hierarchical clustering

Data is already read in from the PCA section

glimpse(Alkaloids_LSmeans)
## Rows: 107
## Columns: 37
## $ Genotype                                 <chr> "CULBPT_05_11", "CULBPT_05_1…
## $ Plot_Source                              <chr> "2K9-8584", "2K9-8622", "2K1…
## $ Species                                  <chr> "Processing", "Processing", …
## $ Class                                    <chr> "Cultivated Processing", "Cu…
## $ Origin                                   <chr> "USA", "USA", "USA", "USA", …
## $ Provence                                 <chr> "NY", "NY", "NY", "NY", "CA"…
## $ Blanca_Cluster1                          <chr> "SLL_processing_2", "SLL_pro…
## $ Blanca_Cluster2                          <chr> "SLL_processing_2", "SLL_pro…
## $ Passport_Species                         <chr> "SLL", "SLL", "SLL", "SLL", …
## $ Passport_Classification                  <chr> "SLL_processing_NY", "SLL_pr…
## $ Sim_Grouping                             <chr> "Arid", "Arid", "Arid", "Ari…
## $ Dehydrotomatidine                        <dbl> 0.000150413, -0.000027200, -…
## $ Tomatidine                               <dbl> 0.004349490, -0.000171592, -…
## $ DehydrotomatineA1                        <dbl> 0.007224074, 0.008654740, 0.…
## $ Dehydrotomatine2                         <dbl> -1.073759e-03, 3.130000e-17,…
## $ TotalDehydrotomatine                     <dbl> 0.006150316, 0.008654740, 0.…
## $ Tomatine                                 <dbl> 0.192910562, 0.282492643, 0.…
## $ Hydroxytomatine1                         <dbl> 1.025210e-02, 6.909610e-04, …
## $ Hydroxytomatine2                         <dbl> 0.15965655, 0.11188176, 0.05…
## $ Hydroxytomatine3                         <dbl> 0.035266551, 0.024394953, 0.…
## $ Hydroxytomatine4                         <dbl> 0.086404221, 0.018934456, 0.…
## $ TotalHydroxytomatine                     <dbl> 0.29179623, 0.15590213, 0.05…
## $ Acetoxytomatine1                         <dbl> -0.068050846, 0.003823208, 0…
## $ Acetoxytomatine2                         <dbl> -0.124244790, 0.046737984, 0…
## $ Acetoxytomatine3                         <dbl> 0.011748097, 0.002473732, -0…
## $ TotalAcetoxytomatine                     <dbl> -0.180547540, 0.053034924, 0…
## $ DehydrolycoperosideFGdehydroesculeosideA <dbl> -0.036985596, 0.006625065, 0…
## $ LycoperosideFGEsculeosideA1              <dbl> 0.005657292, 0.012533403, 0.…
## $ LycoperosideFGEsculeosideA2              <dbl> -0.1693034, 0.3417664, 0.384…
## $ TotalLycoperosideFGEsculeosideA          <dbl> -0.1636461, 0.3542998, 0.399…
## $ EsculeosideB1                            <dbl> -0.034068491, 0.013842904, 0…
## $ EsculeosideB2                            <dbl> -0.006171348, 0.002183259, 0…
## $ EsculeosideB3                            <dbl> -0.037154338, 0.013930950, 0…
## $ TotalEsculeosideB                        <dbl> -0.077394178, 0.029957114, 0…
## $ Total                                    <dbl> 0.03656481, 0.89076768, 0.66…
## $ Latitude                                 <chr> "40.712800000000001", "40.71…
## $ Longitude                                <chr> "-74.006", "-74.006", "-74.0…
# we can use this df for HCA also with some trimming
glimpse(Alkaloids_LSmeans_forPCA)
## Rows: 107
## Columns: 12
## $ Genotype                                 <chr> "CULBPT_05_11", "CULBPT_05_1…
## $ Class                                    <chr> "Cultivated Processing", "Cu…
## $ Dehydrotomatidine                        <dbl> 0.000150413, -0.000027200, -…
## $ Tomatidine                               <dbl> 0.004349490, -0.000171592, -…
## $ TotalDehydrotomatine                     <dbl> 0.006150316, 0.008654740, 0.…
## $ Tomatine                                 <dbl> 0.192910562, 0.282492643, 0.…
## $ TotalHydroxytomatine                     <dbl> 0.29179623, 0.15590213, 0.05…
## $ TotalAcetoxytomatine                     <dbl> -0.180547540, 0.053034924, 0…
## $ DehydrolycoperosideFGdehydroesculeosideA <dbl> -0.036985596, 0.006625065, 0…
## $ TotalLycoperosideFGEsculeosideA          <dbl> -0.1636461, 0.3542998, 0.399…
## $ TotalEsculeosideB                        <dbl> -0.077394178, 0.029957114, 0…
## $ Total                                    <dbl> 0.03656481, 0.89076768, 0.66…

We will move Genotype to a rowname, because in Hierarchical Clustering you can only have your input data (i.e., no meta-data). Having Genotype as a rowname helps recombination with metadata later.

Alkaloids_LSmeans_forHCA <- Alkaloids_LSmeans_forPCA %>%
  column_to_rownames(var = "Genotype") %>%
  select(-Class)

kable(head(Alkaloids_LSmeans_forHCA))
Dehydrotomatidine Tomatidine TotalDehydrotomatine Tomatine TotalHydroxytomatine TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA TotalLycoperosideFGEsculeosideA TotalEsculeosideB Total
CULBPT_05_11 0.0001504 0.0043495 0.0061503 0.1929106 0.2917962 -0.1805475 -0.0369856 -0.1636462 -0.0773942 0.0365648
CULBPT_05_15 -0.0000272 -0.0001716 0.0086547 0.2824926 0.1559021 0.0530349 0.0066251 0.3542998 0.0299571 0.8907677
CULBPT_05_22 -0.0000649 -0.0002516 0.0085205 0.1755250 0.0576202 0.0146705 0.0031468 0.3991363 0.0043489 0.6618186
CULBPT04_1 -0.0000272 0.0001259 -0.0038737 -0.0061446 0.1354779 0.0201870 0.0049377 0.3566699 0.0196277 0.5269806
E6203 -0.0000272 0.0000159 0.0099538 0.2929606 0.0354514 0.0439532 0.0027439 0.3151958 0.0177365 0.7179839
F06-2041 -0.0000272 -0.0001648 -0.0011236 0.0169681 0.0188454 0.0262002 0.0053084 0.1696728 0.0009943 0.2352713

Do we need to scale?

colMeans(Alkaloids_LSmeans_forHCA) # calculate means
##                        Dehydrotomatidine 
##                             1.688489e-04 
##                               Tomatidine 
##                             7.210261e-03 
##                     TotalDehydrotomatine 
##                             1.427984e-01 
##                                 Tomatine 
##                             1.865975e+00 
##                     TotalHydroxytomatine 
##                             1.323755e+00 
##                     TotalAcetoxytomatine 
##                             2.880668e+00 
## DehydrolycoperosideFGdehydroesculeosideA 
##                             1.369470e-01 
##          TotalLycoperosideFGEsculeosideA 
##                             7.396247e+00 
##                        TotalEsculeosideB 
##                             4.298819e-01 
##                                    Total 
##                             1.418294e+01
# 2 means by column-wise
apply(Alkaloids_LSmeans_forHCA, 2, sd) # calculate SDs
##                        Dehydrotomatidine 
##                             5.046878e-04 
##                               Tomatidine 
##                             2.409594e-02 
##                     TotalDehydrotomatine 
##                             3.393789e-01 
##                                 Tomatine 
##                             5.889986e+00 
##                     TotalHydroxytomatine 
##                             2.918240e+00 
##                     TotalAcetoxytomatine 
##                             1.164316e+01 
## DehydrolycoperosideFGdehydroesculeosideA 
##                             1.833925e-01 
##          TotalLycoperosideFGEsculeosideA 
##                             8.536619e+00 
##                        TotalEsculeosideB 
##                             5.177968e-01 
##                                    Total 
##                             1.666068e+01

Yes we do need to scale because we do not have means of 0 and SD of 1.

Scaling

scaled_Alkaloids_LSmeans_forHCA <- scale(Alkaloids_LSmeans_forHCA)

Did the scaling work?

colMeans(scaled_Alkaloids_LSmeans_forHCA) # calculate means, should be 0
##                        Dehydrotomatidine 
##                            -2.885802e-17 
##                               Tomatidine 
##                             5.901302e-18 
##                     TotalDehydrotomatine 
##                            -4.889650e-17 
##                                 Tomatine 
##                            -1.596108e-17 
##                     TotalHydroxytomatine 
##                             1.718511e-17 
##                     TotalAcetoxytomatine 
##                             7.522539e-18 
## DehydrolycoperosideFGdehydroesculeosideA 
##                             5.607858e-17 
##          TotalLycoperosideFGEsculeosideA 
##                             1.686086e-18 
##                        TotalEsculeosideB 
##                            -3.372173e-18 
##                                    Total 
##                            -6.082881e-17
apply(scaled_Alkaloids_LSmeans_forHCA, 2, sd) # calculate SDs, should be 1
##                        Dehydrotomatidine 
##                                        1 
##                               Tomatidine 
##                                        1 
##                     TotalDehydrotomatine 
##                                        1 
##                                 Tomatine 
##                                        1 
##                     TotalHydroxytomatine 
##                                        1 
##                     TotalAcetoxytomatine 
##                                        1 
## DehydrolycoperosideFGdehydroesculeosideA 
##                                        1 
##          TotalLycoperosideFGEsculeosideA 
##                                        1 
##                        TotalEsculeosideB 
##                                        1 
##                                    Total 
##                                        1

Yes scaling worked. Now we can continue.

Generate distance matrix

# calculate the distance matrix (euclidean distance)
dist_matrix <- dist(scaled_Alkaloids_LSmeans_forHCA) 

# conduct hierarchical clustering
alkaloid_hclust <- hclust(d = dist_matrix, method = "ward.D2")

summary(alkaloid_hclust)
##             Length Class  Mode     
## merge       212    -none- numeric  
## height      106    -none- numeric  
## order       107    -none- numeric  
## labels      107    -none- character
## method        1    -none- character
## call          3    -none- call     
## dist.method   1    -none- character

Plot dendrogram of alkaloid_hclust

Make a quick and dirty plot of our hierarchical clustering, will make look nicer later.

hclust_ward2 <- plot(alkaloid_hclust, 
                    main = "HCA of Accessions by Steroidal Alkaloid Profile")

Making dendrogram look nice

You apply dendro_data() on a HCA object, here alkaloid_hclust

# extract data for dendrogram.  can use rectangle or triangle
dend_data <- dendro_data(alkaloid_hclust, type = "rectangle")

# what are the names in dend_data
names(dend_data)
## [1] "segments"    "labels"      "leaf_labels" "class"
# ggplot dendrogram from hclust
ggdendrogram(alkaloid_hclust)

Wrangling end labels

# line segments
head(dend_data$segments) 
##          x        y    xend     yend
## 1 31.59991 24.77930 4.90625 24.77930
## 2  4.90625 24.77930 4.90625 16.95778
## 3  4.90625 16.95778 2.25000 16.95778
## 4  2.25000 16.95778 2.25000 10.14739
## 5  2.25000 10.14739 1.00000 10.14739
## 6  1.00000 10.14739 1.00000  0.00000
# labels
head(dend_data$labels)
##   x y  label
## 1 1 0 LA2308
## 2 2 0 LA2256
## 3 3 0 LA2262
## 4 4 0 LA1654
## 5 5 0 LA2213
## 6 6 0 LA1338
# make vector with the end label order
end_label_order <- dend_data$labels$label

# make df with end_label_order and an id for order
# rename end_label_order to be Genotype to enable joining
end_label_order_withid <- as.data.frame(end_label_order) %>%
  mutate(hclust_end_order = row_number()) %>%
  rename(Genotype = end_label_order)

# convert rowname to column in scaled HCA data to enable joining
scaled_Alkaloids_LSmeans_forHCA_genotype <- 
  as.data.frame(scaled_Alkaloids_LSmeans_forHCA) %>%
  rownames_to_column(var = "Genotype")

class(scaled_Alkaloids_LSmeans_forHCA_genotype)
## [1] "data.frame"
class(end_label_order_withid)
## [1] "data.frame"
# join with meta-data by 
Alkaloids_LSmeans_forHCA_endlabelorder_fordend <- 
  left_join(Alkaloids_LSmeans_withPC, 
            end_label_order_withid, 
            by = "Genotype")

kable(head(Alkaloids_LSmeans_forHCA_endlabelorder_fordend))
Genotype Plot_Source Species Class Origin Provence Blanca_Cluster1 Blanca_Cluster2 Passport_Species Passport_Classification Sim_Grouping Dehydrotomatidine Tomatidine DehydrotomatineA1 Dehydrotomatine2 TotalDehydrotomatine Tomatine Hydroxytomatine1 Hydroxytomatine2 Hydroxytomatine3 Hydroxytomatine4 TotalHydroxytomatine Acetoxytomatine1 Acetoxytomatine2 Acetoxytomatine3 TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA LycoperosideFGEsculeosideA1 LycoperosideFGEsculeosideA2 TotalLycoperosideFGEsculeosideA EsculeosideB1 EsculeosideB2 EsculeosideB3 TotalEsculeosideB Total Latitude Longitude Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 hclust_end_order
CULBPT_05_11 2K9-8584 Processing Cultivated Processing USA NY SLL_processing_2 SLL_processing_2 SLL SLL_processing_NY Arid 0.0001504 0.0043495 0.0072241 -0.0010738 0.0061503 0.1929106 0.0102521 0.1596566 0.0352666 0.0864042 0.2917962 -0.0680508 -0.1242448 0.0117481 -0.1805475 -0.0369856 0.0056573 -0.1693034 -0.1636462 -0.0340685 -0.0061713 -0.0371543 -0.0773942 0.0365648 40.712800000000001 -74.006 -1.520106 -1.1749543 -0.0063469 0.3222487 0.0812080 74
CULBPT_05_15 2K9-8622 Processing Cultivated Processing USA NY SLL_processing_2 SLL_processing_2 SLL SLL_processing_NY Arid -0.0000272 -0.0001716 0.0086547 0.0000000 0.0086547 0.2824926 0.0006910 0.1118818 0.0243950 0.0189345 0.1559021 0.0038232 0.0467380 0.0024737 0.0530349 0.0066251 0.0125334 0.3417664 0.3542998 0.0138429 0.0021833 0.0139310 0.0299571 0.8907677 40.712800000000001 -74.006 -1.506328 -0.9192283 -0.0652522 -0.0874636 0.0303111 45
CULBPT_05_22 2K17-7708-1 Processing Cultivated Processing USA NY SLL_processing_2 SLL_processing_2 SLL SLL_processing_NY Arid -0.0000649 -0.0002516 0.0085205 0.0000000 0.0085205 0.1755250 -0.0001804 0.0505547 0.0040900 0.0023229 0.0576202 0.0078240 0.0069610 -0.0001145 0.0146705 0.0031468 0.0150674 0.3840689 0.3991363 0.0007128 0.0008227 0.0028133 0.0043489 0.6618186 40.712800000000001 -74.006 -1.556597 -0.9465169 -0.0660847 -0.1571217 0.0335051 47
CULBPT04_1 2K9-8566 Processing Cultivated Processing USA NY SLL_processing_2 SLL_processing_2 SLL SLL_processing_NY Arid -0.0000272 0.0001259 -0.0038737 0.0000000 -0.0038737 -0.0061446 0.0015930 0.1059454 0.0195829 0.0083566 0.1354779 0.0098585 0.0095344 0.0007941 0.0201870 0.0049377 0.0100416 0.3466282 0.3566699 0.0100830 0.0006432 0.0089015 0.0196277 0.5269806 40.712800000000001 -74.006 -1.554353 -0.9098403 -0.0820367 -0.0704848 0.0509933 51
E6203 2K9-8600 Processing Cultivated Processing USA CA SLL_processing_1 SLL_processing_1_1 SLL SLL_processing_CA Arid -0.0000272 0.0000159 0.0099538 0.0000000 0.0099538 0.2929606 0.0000000 0.0227075 0.0039160 0.0088279 0.0354514 0.0002365 0.0395374 0.0041794 0.0439532 0.0027439 0.0133225 0.3018732 0.3151958 0.0080894 -0.0003574 0.0100045 0.0177365 0.7179839 36.778300000000002 -119.4179 -1.530189 -0.9439456 -0.0437656 -0.0891376 0.0065314 43
F06-2041 2K16-9843 Processing Cultivated Processing USA OH SLL_processing_1 SLL_processing_1_3 SLL SLL_processing_OH Humid -0.0000272 -0.0001648 -0.0011236 0.0000000 -0.0011236 0.0169681 0.0011237 0.0195573 -0.0016308 -0.0016069 0.0188454 -0.0004237 0.0271524 -0.0005286 0.0262002 0.0053084 0.0049033 0.1647694 0.1696728 -0.0006298 -0.0003074 0.0019315 0.0009943 0.2352713 40.417299999999997 -82.9071 -1.585830 -0.9382978 -0.0679953 -0.0773861 0.0191808 49
# arrange by end label order
order_for_Genotype_endlabels <- 
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend %>%
  arrange(hclust_end_order)

order_for_Genotype_endlabels$Class <- factor(order_for_Genotype_endlabels$Class, 
                                                 levels = c("Cultivated Processing", 
                                                            "Cultivated Cherry", 
                                                            "Wide Cross Hybrid", 
                                                            "Wild Cherry", 
                                                            "S. pimpinellifolium"))

HCA_tomato_alkaloids_wardD2 <- dend_data$segments %>%
  ggplot(aes(x = x, y = y)) +
  geom_segment(aes(xend = xend, yend = yend)) +
  geom_text(data = dend_data$labels, 
            aes(x, y, label = order_for_Genotype_endlabels$Genotype, 
                color = order_for_Genotype_endlabels$Class), 
            hjust = 1, angle = 90, size = 2.5) +
  scale_color_manual(values = c("purple", "blue", "limegreen", "gold", "red2")) +
  ylim(-5, 25) +
  theme_minimal() +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank()) +
  labs(title = "Dendrogram from Hierarhical Clustering of Tomato Steroidal Alkaloids",
       subtitle = "Ward's D2 Linkage Distances",
       x = "",
       y = "Height",
       color = "Class")

HCA_tomato_alkaloids_wardD2

Cut tree at k = 3

Selected based on major clusters of the dendrogram above.

cut3.alkaloid_hclust <- cutree(alkaloid_hclust, k = 3)
hclust_3_group <- cut3.alkaloid_hclust

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend %>%
  mutate(hclust_3_cluster = as.factor(hclust_3_group))

glimpse(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust)
## Rows: 107
## Columns: 44
## $ Genotype                                 <chr> "CULBPT_05_11", "CULBPT_05_1…
## $ Plot_Source                              <chr> "2K9-8584", "2K9-8622", "2K1…
## $ Species                                  <chr> "Processing", "Processing", …
## $ Class                                    <fct> Cultivated Processing, Culti…
## $ Origin                                   <chr> "USA", "USA", "USA", "USA", …
## $ Provence                                 <chr> "NY", "NY", "NY", "NY", "CA"…
## $ Blanca_Cluster1                          <chr> "SLL_processing_2", "SLL_pro…
## $ Blanca_Cluster2                          <chr> "SLL_processing_2", "SLL_pro…
## $ Passport_Species                         <chr> "SLL", "SLL", "SLL", "SLL", …
## $ Passport_Classification                  <chr> "SLL_processing_NY", "SLL_pr…
## $ Sim_Grouping                             <chr> "Arid", "Arid", "Arid", "Ari…
## $ Dehydrotomatidine                        <dbl> 0.000150413, -0.000027200, -…
## $ Tomatidine                               <dbl> 0.004349490, -0.000171592, -…
## $ DehydrotomatineA1                        <dbl> 0.007224074, 0.008654740, 0.…
## $ Dehydrotomatine2                         <dbl> -1.073759e-03, 3.130000e-17,…
## $ TotalDehydrotomatine                     <dbl> 0.006150316, 0.008654740, 0.…
## $ Tomatine                                 <dbl> 0.192910562, 0.282492643, 0.…
## $ Hydroxytomatine1                         <dbl> 1.025210e-02, 6.909610e-04, …
## $ Hydroxytomatine2                         <dbl> 0.15965655, 0.11188176, 0.05…
## $ Hydroxytomatine3                         <dbl> 0.035266551, 0.024394953, 0.…
## $ Hydroxytomatine4                         <dbl> 0.086404221, 0.018934456, 0.…
## $ TotalHydroxytomatine                     <dbl> 0.29179623, 0.15590213, 0.05…
## $ Acetoxytomatine1                         <dbl> -0.068050846, 0.003823208, 0…
## $ Acetoxytomatine2                         <dbl> -0.124244790, 0.046737984, 0…
## $ Acetoxytomatine3                         <dbl> 0.011748097, 0.002473732, -0…
## $ TotalAcetoxytomatine                     <dbl> -0.180547540, 0.053034924, 0…
## $ DehydrolycoperosideFGdehydroesculeosideA <dbl> -0.036985596, 0.006625065, 0…
## $ LycoperosideFGEsculeosideA1              <dbl> 0.005657292, 0.012533403, 0.…
## $ LycoperosideFGEsculeosideA2              <dbl> -0.1693034, 0.3417664, 0.384…
## $ TotalLycoperosideFGEsculeosideA          <dbl> -0.1636461, 0.3542998, 0.399…
## $ EsculeosideB1                            <dbl> -0.034068491, 0.013842904, 0…
## $ EsculeosideB2                            <dbl> -0.006171348, 0.002183259, 0…
## $ EsculeosideB3                            <dbl> -0.037154338, 0.013930950, 0…
## $ TotalEsculeosideB                        <dbl> -0.077394178, 0.029957114, 0…
## $ Total                                    <dbl> 0.03656481, 0.89076768, 0.66…
## $ Latitude                                 <chr> "40.712800000000001", "40.71…
## $ Longitude                                <chr> "-74.006", "-74.006", "-74.0…
## $ Dim.1                                    <dbl> -1.5201064, -1.5063278, -1.5…
## $ Dim.2                                    <dbl> -1.17495432, -0.91922826, -0…
## $ Dim.3                                    <dbl> -0.006346942, -0.065252226, …
## $ Dim.4                                    <dbl> 0.32224868, -0.08746361, -0.…
## $ Dim.5                                    <dbl> 0.081207963, 0.030311083, 0.…
## $ hclust_end_order                         <int> 74, 45, 47, 51, 43, 49, 50, …
## $ hclust_3_cluster                         <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…

Superimpose clusters onto PCA

Regular PCA (no cluster assignment)

We have made this plot already in the PCA section of this Rmd.

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust$Class <- 
  factor(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust$Class, 
         levels = c("Cultivated Processing", 
                    "Cultivated Cherry", 
                    "Wide Cross Hybrid", 
                    "Wild Cherry", 
                    "S. pimpinellifolium"))

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class)) +
  geom_point(size = 2, alpha = 0.8) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  scale_shape_manual(values=c(24,22,23,22,21),
                     labels = expression("Cultivated Processing", 
                                         "Cultivated Cherry", 
                                         "Wide Cross Hybrid", 
                                         "Wild Cherry", italic("S. pimpinellifolium"))) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = expression("Cultivated Processing", 
                                        "Cultivated Cherry", 
                                        "Wide Cross Hybrid", 
                                        "Wild Cherry", 
                                        italic("S. pimpinellifolium"))) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(title = "PCA Scores Plot",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") 

Labeling points with their genotype.

alkaloid_pca <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class, label = Genotype)) +
  geom_point() +
  geom_text(nudge_x = 0.8, nudge_y = 0.1) +
  scale_shape_manual(values=c(24,22,23,22,21)) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2")) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12)) +
  labs(title = "",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%")

alkaloid_pca

A disaster. Using ggrepel doesn’t work super well here either because there is so much bunching of points.

Let’s try using plotly.

alkaloid_pca <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, 
             fill = Class, shape = Class, label = Genotype, text = Genotype)) +
  geom_point(size = 2, alpha = 0.8) +
  scale_shape_manual(values=c(24,22,23,22,21)) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2")) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12)) +
  labs(title = "PCA Scores Plot",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%")

ggplotly(alkaloid_pca, tooltip = "text")

PCA with HCA Clusters

3 clusters

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = hclust_3_cluster, shape = Class)) +
  geom_point(size = 2, alpha = 0.8) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  scale_shape_manual(values=c(24,22,23,22,21),
                     labels = expression("Cultivated Processing", 
                                         "Cultivated Cherry", 
                                         "Wide Cross Hybrid", 
                                         "Wild Cherry", 
                                         italic("S. pimpinellifolium"))) +
  scale_fill_viridis_d() +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(title = "PCA Scores Plot",
       subtitle = "Overlaid with Hierarchical Cluster Assignment (3 clusters)",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") 

PCA of wild cherry only

Remove all other accessions that do not belong to the class wild cherry.

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry <- 
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  filter(Class == "Wild Cherry")

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class)) +
  geom_point(size = 2.5, alpha = 0.8) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  scale_shape_manual(values=22) +
  scale_fill_manual(values = "gold")  +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(title = "",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") + 
  theme(legend.position="right") 

HCA overlaid on wild cherry

3 clusters

glimpse(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry)
## Rows: 31
## Columns: 44
## $ Genotype                                 <chr> "LA1314", "LA1338", "LA1464"…
## $ Plot_Source                              <chr> "SG13-403x", "SG13-404x", "S…
## $ Species                                  <chr> "Wild Cherry", "Wild Cherry"…
## $ Class                                    <fct> Wild Cherry, Wild Cherry, Wi…
## $ Origin                                   <chr> "Peru", "Ecuador", "Honduras…
## $ Provence                                 <chr> "Cusco", "Napo", "Unknown", …
## $ Blanca_Cluster1                          <chr> "SLC_Peru", "SLC_Ecuador", "…
## $ Blanca_Cluster2                          <chr> "SLC_Peru_3", "SLC_Ecuador_1…
## $ Passport_Species                         <chr> "SLC", "SLC", "SLC", "SLC", …
## $ Passport_Classification                  <chr> "SLC_Peru_I3", "SLC", "SLC_M…
## $ Sim_Grouping                             <chr> "Not_Classified", "Not_Class…
## $ Dehydrotomatidine                        <dbl> 0.000094700, 0.000755740, 0.…
## $ Tomatidine                               <dbl> 0.002853565, 0.029799904, 0.…
## $ DehydrotomatineA1                        <dbl> 0.11314257, 0.18169144, 0.21…
## $ Dehydrotomatine2                         <dbl> 2.116532e-03, 1.231448e-03, …
## $ TotalDehydrotomatine                     <dbl> 0.11525910, 0.18292289, 0.22…
## $ Tomatine                                 <dbl> 1.04216262, 2.01065486, 1.93…
## $ Hydroxytomatine1                         <dbl> 4.714502e-02, 9.745796e-01, …
## $ Hydroxytomatine2                         <dbl> 0.9868761, 1.7829255, 1.5262…
## $ Hydroxytomatine3                         <dbl> 0.15099070, 19.57380644, 0.5…
## $ Hydroxytomatine4                         <dbl> 0.18403370, 3.37755866, 1.00…
## $ TotalHydroxytomatine                     <dbl> 1.3690455, 25.7088702, 3.184…
## $ Acetoxytomatine1                         <dbl> 0.211834825, -0.003449198, 0…
## $ Acetoxytomatine2                         <dbl> 0.337686456, -0.002722664, 0…
## $ Acetoxytomatine3                         <dbl> 0.074216019, -0.000528550, 0…
## $ TotalAcetoxytomatine                     <dbl> 0.623737300, -0.006700412, 0…
## $ DehydrolycoperosideFGdehydroesculeosideA <dbl> 0.121574301, -0.001452750, 0…
## $ LycoperosideFGEsculeosideA1              <dbl> 0.660210064, -0.002774051, 1…
## $ LycoperosideFGEsculeosideA2              <dbl> 8.473063024, -0.200172079, 1…
## $ TotalLycoperosideFGEsculeosideA          <dbl> 9.13327309, -0.20294613, 20.…
## $ EsculeosideB1                            <dbl> 0.148504676, -0.005328571, 0…
## $ EsculeosideB2                            <dbl> 0.025751543, -0.000948925, 0…
## $ EsculeosideB3                            <dbl> 0.213715674, -0.004572162, 0…
## $ TotalEsculeosideB                        <dbl> 0.387971894, -0.010849657, 1…
## $ Total                                    <dbl> 12.795972, 27.711055, 28.352…
## $ Latitude                                 <chr> "-13.532", "-1.483333", "15.…
## $ Longitude                                <chr> "-71.967500000000001", "-78"…
## $ Dim.1                                    <dbl> -0.2505624921, 2.9834103837,…
## $ Dim.2                                    <dbl> 0.1341207, -1.9245262, 2.605…
## $ Dim.3                                    <dbl> -0.04667415, -3.34437043, 0.…
## $ Dim.4                                    <dbl> -0.112968939, 2.569235923, -…
## $ Dim.5                                    <dbl> 0.07858090, 6.00800998, 0.23…
## $ hclust_end_order                         <int> 23, 6, 102, 101, 87, 15, 18,…
## $ hclust_3_cluster                         <fct> 1, 3, 2, 2, 2, 1, 1, 2, 2, 2…
(PCA_3_HCA <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  ggplot(aes(x = Dim.1, y = Dim.2, 
             fill = hclust_3_cluster, 
             label = hclust_3_cluster)) +
  geom_point(size = 2.5, alpha = 0.8, shape = 22) +
  scale_fill_viridis_d() +
  labs(title = "PCA Scores Plot Colored by Hierarchical Cluster",
       subtitle = "Ward's D2 Linkage, Wild Cherry Only",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.position = "none"))

ggsave(filename = "HCA_3clusters.png",
       plot = PCA_3_HCA,
       dpi = 1200,
       width = 9,
       height = 6)

3 clusters with accessions labelled

Labelling the accessions in the bottom cluster.

kable(head(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry))
Genotype Plot_Source Species Class Origin Provence Blanca_Cluster1 Blanca_Cluster2 Passport_Species Passport_Classification Sim_Grouping Dehydrotomatidine Tomatidine DehydrotomatineA1 Dehydrotomatine2 TotalDehydrotomatine Tomatine Hydroxytomatine1 Hydroxytomatine2 Hydroxytomatine3 Hydroxytomatine4 TotalHydroxytomatine Acetoxytomatine1 Acetoxytomatine2 Acetoxytomatine3 TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA LycoperosideFGEsculeosideA1 LycoperosideFGEsculeosideA2 TotalLycoperosideFGEsculeosideA EsculeosideB1 EsculeosideB2 EsculeosideB3 TotalEsculeosideB Total Latitude Longitude Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 hclust_end_order hclust_3_cluster
LA1314 SG13-403x Wild Cherry Wild Cherry Peru Cusco SLC_Peru SLC_Peru_3 SLC SLC_Peru_I3 Not_Classified 0.0000947 0.0028536 0.1131426 0.0021165 0.1152591 1.0421626 0.0471450 0.9868761 0.1509907 0.1840337 1.3690455 0.2118348 0.3376865 0.0742160 0.6237373 0.1215743 0.6602101 8.4730630 9.1332731 0.1485047 0.0257515 0.2137157 0.3879719 12.79597 -13.532 -71.967500000000001 -0.2505625 0.1341207 -0.0466742 -0.1129689 0.0785809 23 1
LA1338 SG13-404x Wild Cherry Wild Cherry Ecuador Napo SLC_Ecuador SLC_Ecuador_1 SLC SLC Not_Classified 0.0007557 0.0297999 0.1816914 0.0012314 0.1829229 2.0106549 0.9745796 1.7829255 19.5738064 3.3775587 25.7088702 -0.0034492 -0.0027227 -0.0005286 -0.0067004 -0.0014528 -0.0027741 -0.2001721 -0.2029461 -0.0053286 -0.0009489 -0.0045722 -0.0108497 27.71105 -1.483333 -78 2.9834104 -1.9245262 -3.3443704 2.5692359 6.0080100 6 3
LA1464 SG13-407x Wild Cherry Wild Cherry Honduras Unknown SLC_non_Andean SLC_Mesoamerica SLC SLC_Mesoamerica Not_Classified 0.0000961 0.0039670 0.2192760 0.0061383 0.2254143 1.9370674 0.0894665 1.5262340 0.5604863 1.0083278 3.1847315 0.2090763 0.3367693 0.1678508 0.7136964 0.4518280 1.6764799 18.7002105 20.3766904 0.5121216 0.1122165 0.8350692 1.4594073 28.35268 15.2 -86.241900000000001 1.6899045 2.6052804 0.0632207 -0.5510405 0.2328861 102 2
LA1512 SG13-408x Wild Cherry Wild Cherry El Salvador Unknown SLC_non_Andean SLC_Mesoamerica SLC SLC_Mesoamerica Not_Classified -0.0000111 0.0024415 0.0165522 0.0000000 0.0165522 0.1963570 0.0707200 1.1800452 0.2465679 0.1930546 1.6903876 0.1278093 0.0832638 0.0141662 0.2252393 0.2895555 1.6448154 15.4073864 17.0522018 0.4789698 0.1101663 0.7379315 1.3270677 20.79979 13.7942 -88.896500000000003 0.5474504 2.1285400 -0.0133431 -0.4664860 0.1442798 101 2
LA1542 SG14-102x Wild Cherry Wild Cherry Costa Rica Unknown SLC_non_Andean SLC_Costa_Rica SLC SLC Not_Classified 0.0000344 0.0147139 0.1001867 0.0010964 0.1012830 1.1893261 0.0399890 1.4806166 0.2776032 0.6097367 2.4079455 0.2565412 0.2979414 0.1174430 0.6719255 0.4330046 2.0954784 20.9710741 23.0665526 0.3705859 0.0620427 0.6684554 1.1010841 28.98587 9.9 -83.666667000000004 1.4629241 2.4753846 0.1722504 -0.6019816 0.3938678 87 2
LA1549 SG13-411x Wild Cherry Wild Cherry Peru Pasco SLC_Peru SLC_Peru_2 SLC SLC Not_Classified -0.0000006 0.0001202 0.0574876 0.0006432 0.0581308 0.1689783 0.0082920 0.4320073 0.1008166 0.2264037 0.7675196 0.0890501 0.1289289 0.0407354 0.2587144 0.1400177 0.2159879 3.6698807 3.8858686 0.1235214 0.0188230 0.1404762 0.2828206 5.56217 -10.583333 -75.483333000000002 -0.8856488 -0.1391765 -0.0634522 -0.2028354 0.0823163 15 1
# create character vector of Genotypes in bottom cluster
bottom_wild_cherry_forfilt <- c("LA2126A",
                       "LA1338",
                        "LA1654",
                        "LA2262",
                        "LA2213",
                        "LA2256",
                        "LA2308",
                        "LA1701",
                        "PI155372",
                        "PI129128")

# create new df which only includes those bottom cluster Genotypes
Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry_bottom <-
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  filter(Genotype %in% bottom_wild_cherry_forfilt)

dim(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry_bottom)
## [1] 10 44
# get the same location of labels each time
set.seed(123) # because geom_label_repel iterates

(PCA_3_HCA_bottomclusterlabelled <- 
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  ggplot(aes(x = Dim.1, y = Dim.2, 
             fill = hclust_3_cluster, 
             label = hclust_3_cluster)) +
  geom_point(size = 2.5, alpha = 0.8, shape = 22) +
  geom_label_repel(data = Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry_bottom,
                   aes(label = Genotype), fill = "white") +
  scale_fill_viridis_d() +
  labs(title = "PCA Scores Plot Colored by Hierarchical Cluster",
       subtitle = "Ward's D2 Linkage, Wild Cherry Only",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.position = "none"))

ggsave(filename = "HCA_3clusters_bottomclusterlabelled.jpg",
       plot = PCA_3_HCA_bottomclusterlabelled,
       dpi = 1200,
       width = 9,
       height = 6)

GWAS

UPLOADING SOON.

Manhattan Plots

Create Manhattan plots from the GWAS and mQTL analysis in our diversity panel and bi-parental populations, respectively. Selection criteria included known patterns of genetic diversity as determined by genotyping using a 7,720 SNP array (Sim et al., 2012a; Blanca et al., 2015), previous phenotypic information for alkaloid content (Rick et al., 1994), and geographic origin (visualized in Fig. S1.). Alkaloids were phenotyped using our previously published method (Dzakovich et al., 2020).

Diversity panel GWAS

Read in data

diversity_GWAS_raw <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "GWAS Output_MAF10Pct")

# what are the dimensions of this data?
dim(diversity_GWAS_raw)
## [1] 5403   15
# what is the structure of this dataframe?
str(diversity_GWAS_raw)
## tibble [5,403 × 15] (S3: tbl_df/tbl/data.frame)
##  $ MARKER                                                    : chr [1:5403] "solcap_snp_sl_15058" "solcap_snp_sl_60635" "solcap_snp_sl_60604" "solcap_snp_sl_15056" ...
##  $ LOCUS                                                     : num [1:5403] 1 2 3 4 5 6 7 8 9 10 ...
##  $ CHR                                                       : num [1:5403] 1 1 1 1 1 1 1 1 1 1 ...
##  $ Phy_Posbp                                                 : num [1:5403] 60975 62673 206094 274511 313487 ...
##  $ Ch_Mb                                                     : num [1:5403] 1 1 1 1 1 ...
##  $ Dehydrotomatidine_neg_log10_pval                          : num [1:5403] 0.0143 0 0 0 0 ...
##  $ Tomatidine_neg_log10_pval                                 : num [1:5403] 0.15 0 0 0 0 ...
##  $ Dehydrotomatine_neg_log10_pval                            : num [1:5403] 0 0 0 0 0 0 0 0 0 0 ...
##  $ Alpha_Tomatine_neg_log10_pval                             : num [1:5403] 0 0 0 0 0 0 0 0 0 0 ...
##  $ Hydroxytomatine_neg_log10_pval                            : num [1:5403] 0 0 0 0 0 0 0 0 0 0 ...
##  $ Acetoxytomatine_neg_log10_pval                            : num [1:5403] 0.00763 0 0 0 0 ...
##  $ DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval: num [1:5403] 0.00182 0.00802 0 0 0 ...
##  $ LycoperosideF_G_Esculeoside_A_neg_log10_pval              : num [1:5403] 0 0 0 0 0 0 0 0 0 0 ...
##  $ Esculeoside_B_neg_log10_pval                              : num [1:5403] 0 0 0 0 0 0 0 0 0 0 ...
##  $ Total_neg_log10_pval                                      : num [1:5403] 0 0 0 0 0 0 0 0 0 0 ...

Wrangling

Making data tidy facilitates creating Manhattan plots that are faceted by each different alkaloid.

diversity_GWAS_tidy <- diversity_GWAS_raw %>%
  pivot_longer(cols = Dehydrotomatidine_neg_log10_pval:Total_neg_log10_pval, 
               names_to = "alkaloid", 
               values_to = "pvalue")

# check dataframe dimensions
dim(diversity_GWAS_tidy)
## [1] 54030     7

Setting up labels for use with labeller.

alk_labels <- c(
  Dehydrotomatidine_neg_log10_pval = "Dehydrotomatidine",
  Tomatidine_neg_log10_pval = "Tomatidine",
  Dehydrotomatine_neg_log10_pval = "Dehydrotomatine",
  Alpha_Tomatine_neg_log10_pval = "Alpha-Tomatine",
  Hydroxytomatine_neg_log10_pval = "Hydroxytomatine",
  Acetoxytomatine_neg_log10_pval = "Acetoxytomatine",
  DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval = "Dehydrolycoperoside F/G/Dehydroesculeoside A",
  LycoperosideF_G_Esculeoside_A_neg_log10_pval = "Lycoperoside F/G/Esculeoside A",
  Esculeoside_B_neg_log10_pval = "Esculeoside B",
  Total_neg_log10_pval = "Total Steroidal Alkaloids")

Alkaloid as factors

This will make our faceted plot go in the alkaloid biosynthetic pathway order, instead of alphabetically.

diversity_GWAS_tidy$alkaloid <- 
  factor(diversity_GWAS_tidy$alkaloid, 
         levels = c("Dehydrotomatidine_neg_log10_pval", 
                    "Tomatidine_neg_log10_pval", 
                    "Dehydrotomatine_neg_log10_pval", 
                    "Alpha_Tomatine_neg_log10_pval", 
                    "Hydroxytomatine_neg_log10_pval", 
                    "Acetoxytomatine_neg_log10_pval", 
                    "DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval",
                    "LycoperosideF_G_Esculeoside_A_neg_log10_pval",
                    "Esculeoside_B_neg_log10_pval",
                    "Total_neg_log10_pval"))

str(diversity_GWAS_tidy)
## tibble [54,030 × 7] (S3: tbl_df/tbl/data.frame)
##  $ MARKER   : chr [1:54030] "solcap_snp_sl_15058" "solcap_snp_sl_15058" "solcap_snp_sl_15058" "solcap_snp_sl_15058" ...
##  $ LOCUS    : num [1:54030] 1 1 1 1 1 1 1 1 1 1 ...
##  $ CHR      : num [1:54030] 1 1 1 1 1 1 1 1 1 1 ...
##  $ Phy_Posbp: num [1:54030] 60975 60975 60975 60975 60975 ...
##  $ Ch_Mb    : num [1:54030] 1 1 1 1 1 ...
##  $ alkaloid : Factor w/ 10 levels "Dehydrotomatidine_neg_log10_pval",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ pvalue   : num [1:54030] 0.0143 0.1498 0 0 0 ...
class(diversity_GWAS_tidy$alkaloid)
## [1] "factor"
levels(diversity_GWAS_tidy$alkaloid)
##  [1] "Dehydrotomatidine_neg_log10_pval"                          
##  [2] "Tomatidine_neg_log10_pval"                                 
##  [3] "Dehydrotomatine_neg_log10_pval"                            
##  [4] "Alpha_Tomatine_neg_log10_pval"                             
##  [5] "Hydroxytomatine_neg_log10_pval"                            
##  [6] "Acetoxytomatine_neg_log10_pval"                            
##  [7] "DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval"
##  [8] "LycoperosideF_G_Esculeoside_A_neg_log10_pval"              
##  [9] "Esculeoside_B_neg_log10_pval"                              
## [10] "Total_neg_log10_pval"

Faceting by alkaloid GWAS

Here we have one mondo figure with all the alkaloid Manhattan plots on the same axis. Points are colored red if they are significant at the 0.05 level after a FDR correction. The input data are FDR corrected, and a -log10pvalue of 1.301 is the same as P = 0.05 FDR corrected.

diversity_GWAS_tidy %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), y = pvalue, group = alkaloid)) +
  geom_point(aes(color = pvalue > 1.301), size=.5, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = c("black", "red")) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("-log"[10]* " FDR Corrected P-Value")) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28))) 

Alternating chromosome colors

This will make it easier to tell which markers are on which chromosome.

diversity_GWAS_alt_chr <- diversity_GWAS_tidy %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(CHR), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free_x") 

diversity_GWAS_alt_chr

ggsave("Diversity_GWAS_alt_chr.png",
       plot = diversity_GWAS_alt_chr,
       dpi = 1200,
       width = 10,
       height = 6)

Free y-axis

All y-axis scales are independent.

diversity_GWAS_alt_chr_free_y <- diversity_GWAS_tidy %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(CHR), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free") 

diversity_GWAS_alt_chr_free_y

ggsave("Diversity_GWAS_alt_chr_free_y.png",
       plot = diversity_GWAS_alt_chr_free_y,
       dpi = 1200,
       width = 10,
       height = 6)

Individual Manhattan plots for each alkaloid

kable(head(diversity_GWAS_raw))
MARKER LOCUS CHR Phy_Posbp Ch_Mb Dehydrotomatidine_neg_log10_pval Tomatidine_neg_log10_pval Dehydrotomatine_neg_log10_pval Alpha_Tomatine_neg_log10_pval Hydroxytomatine_neg_log10_pval Acetoxytomatine_neg_log10_pval DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval LycoperosideF_G_Esculeoside_A_neg_log10_pval Esculeoside_B_neg_log10_pval Total_neg_log10_pval
solcap_snp_sl_15058 1 1 60975 1.000610 0.0142731 0.1498227 0 0 0 0.0076282 0.0018163 0 0 0
solcap_snp_sl_60635 2 1 62673 1.000627 0.0000000 0.0000000 0 0 0 0.0000000 0.0080247 0 0 0
solcap_snp_sl_60604 3 1 206094 1.002061 0.0000000 0.0000000 0 0 0 0.0000000 0.0000000 0 0 0
solcap_snp_sl_15056 4 1 274511 1.002745 0.0000000 0.0000000 0 0 0 0.0000000 0.0000000 0 0 0
solcap_snp_sl_15055 5 1 313487 1.003135 0.0000000 0.0000000 0 0 0 0.0000000 0.0000000 0 0 0
solcap_snp_sl_15054 6 1 313633 1.003136 0.0000000 0.0000000 0 0 0 0.0000000 0.0000000 0 0 0
diversity_GWAS_manhattan_plots <- function(g){
diversity_GWAS_raw %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), color = as.factor(CHR))) +
  geom_point(aes_(y = as.name(g)), size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  labs(title = paste(g),
       x ="Chromosome",
       y = (expression("FDR corrected "-log[10](P))))
}  

all_diversity_gwas_manhatan_plots <- lapply(names(diversity_GWAS_raw[,6:ncol(diversity_GWAS_raw)]), 
                                            diversity_GWAS_manhattan_plots)
all_diversity_gwas_manhatan_plots
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Bi-parental GWAS

Read in data

biparental_GWAS_raw <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "BC1S1 Output MAF10Pct")

# what are the dimensions of this data?
dim(biparental_GWAS_raw)
## [1] 139  17
# what is the structure of this dataframe?
str(biparental_GWAS_raw)
## tibble [139 × 17] (S3: tbl_df/tbl/data.frame)
##  $ MARKER                                                    : chr [1:139] "solcap_snp_sl_60078" "solcap_snp_sl_59771" "solcap_snp_sl_13762" "solcap_snp_sl_43632" ...
##  $ Chr                                                       : num [1:139] 1 1 1 1 1 2 2 2 2 2 ...
##  $ Phy_Posbp                                                 : num [1:139] 2955601 6229659 67855513 77935539 87977912 ...
##  $ Ch_Mb                                                     : num [1:139] 1.03 1.06 1.68 1.78 1.88 ...
##  $ Gen_PoscM                                                 : chr [1:139] "NA" "NA" "41.666899999999998" "NA" ...
##  $ Phy_Chr                                                   : chr [1:139] "SL2.40ch01" "SL2.40ch01" "SL2.40ch01" "SL2.40ch01" ...
##  $ cM                                                        : chr [1:139] "NA" "NA" "0.320514615" "NA" ...
##  $ Dehydrotomatidine_neg_log10_pval                          : num [1:139] 0.0216 0.1346 0.0285 0.1346 0.1346 ...
##  $ Tomatidine_neg_log10_pval                                 : num [1:139] 0.2062 0.1159 0.1319 0.0922 0.9355 ...
##  $ Dehydrotomatine_neg_log10_pval                            : num [1:139] 0.175 0.175 0.175 0.175 0.282 ...
##  $ Alpha_Tomatine_neg_log10_pval                             : num [1:139] 0.0289 0.0289 0.0289 0.1195 0.2449 ...
##  $ Hydroxytomatine_neg_log10_pval                            : num [1:139] 0.295 0.425 0.295 0.961 0.295 ...
##  $ Acetoxytomatine_neg_log10_pval                            : num [1:139] 0.155 0.171 0.155 0.175 0.327 ...
##  $ DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval: num [1:139] 0.313 0.727 0.203 0.727 0.15 ...
##  $ LycoperosideF_G_Esculeoside_A_neg_log10_pval              : num [1:139] 0.368 0.488 0.198 1.206 0.21 ...
##  $ Esculeoside_B_neg_log10_pval                              : num [1:139] 0.13 0.51 0.51 0.482 0.13 ...
##  $ Total_neg_log10_pval                                      : num [1:139] 0.0711 0.0711 0.0711 0.2056 0.2056 ...

Wrangling

Making data tidy facilitates faceting by alkaloids.

biparental_GWAS_tidy <- biparental_GWAS_raw %>%
  pivot_longer(cols = Dehydrotomatidine_neg_log10_pval:Total_neg_log10_pval, 
               names_to = "alkaloid", 
               values_to = "pvalue")

# check dataframe dimensions
dim(biparental_GWAS_tidy)
## [1] 1390    9

Alkaloid as factors

This will make our faceted plot go in the alkaloid biosynthetic pathway order, instead of alphabetically.

biparental_GWAS_tidy$alkaloid <- 
  factor(biparental_GWAS_tidy$alkaloid, 
         levels = c("Dehydrotomatidine_neg_log10_pval", 
                    "Tomatidine_neg_log10_pval", 
                    "Dehydrotomatine_neg_log10_pval", 
                    "Alpha_Tomatine_neg_log10_pval", 
                    "Hydroxytomatine_neg_log10_pval", 
                    "Acetoxytomatine_neg_log10_pval", 
                    "DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval",
                    "LycoperosideF_G_Esculeoside_A_neg_log10_pval",
                    "Esculeoside_B_neg_log10_pval",
                    "Total_neg_log10_pval"))

str(biparental_GWAS_tidy)
## tibble [1,390 × 9] (S3: tbl_df/tbl/data.frame)
##  $ MARKER   : chr [1:1390] "solcap_snp_sl_60078" "solcap_snp_sl_60078" "solcap_snp_sl_60078" "solcap_snp_sl_60078" ...
##  $ Chr      : num [1:1390] 1 1 1 1 1 1 1 1 1 1 ...
##  $ Phy_Posbp: num [1:1390] 2955601 2955601 2955601 2955601 2955601 ...
##  $ Ch_Mb    : num [1:1390] 1.03 1.03 1.03 1.03 1.03 ...
##  $ Gen_PoscM: chr [1:1390] "NA" "NA" "NA" "NA" ...
##  $ Phy_Chr  : chr [1:1390] "SL2.40ch01" "SL2.40ch01" "SL2.40ch01" "SL2.40ch01" ...
##  $ cM       : chr [1:1390] "NA" "NA" "NA" "NA" ...
##  $ alkaloid : Factor w/ 10 levels "Dehydrotomatidine_neg_log10_pval",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ pvalue   : num [1:1390] 0.0216 0.2062 0.1746 0.0289 0.2954 ...
class(biparental_GWAS_tidy$alkaloid)
## [1] "factor"
levels(biparental_GWAS_tidy$alkaloid)
##  [1] "Dehydrotomatidine_neg_log10_pval"                          
##  [2] "Tomatidine_neg_log10_pval"                                 
##  [3] "Dehydrotomatine_neg_log10_pval"                            
##  [4] "Alpha_Tomatine_neg_log10_pval"                             
##  [5] "Hydroxytomatine_neg_log10_pval"                            
##  [6] "Acetoxytomatine_neg_log10_pval"                            
##  [7] "DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval"
##  [8] "LycoperosideF_G_Esculeoside_A_neg_log10_pval"              
##  [9] "Esculeoside_B_neg_log10_pval"                              
## [10] "Total_neg_log10_pval"

Faceting by alkaloid GWAS

Here we have one mondo figure with all the alkaloid Manhattan plots on the same axis. Points are colored red if they are significant at the 0.05 level after a FDR correction. The input data are FDR corrected, and a -log10pvalue of 1.301 is the same as P = 0.05 FDR corrected.

biparental_GWAS_tidy %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), y = pvalue, group = alkaloid)) +
  geom_point(aes(color = pvalue > 1.301), size=.5, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_tidy$Chr), 
                     label = unique(biparental_GWAS_tidy$Chr)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = c("black", "red")) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("-log"[10]* " FDR Corrected P-Value")) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28))) 

Alternating chromosome colors

biparental_GWAS_alt_chr <- biparental_GWAS_tidy %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(Chr), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_tidy$Chr), 
                     label = unique(biparental_GWAS_tidy$Chr)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free_x") 

biparental_GWAS_alt_chr

ggsave("biparental_GWAS_alt_chr.png",
       plot = biparental_GWAS_alt_chr,
       dpi = 1200,
       width = 10,
       height = 6)

Free y-axis

biparental_GWAS_alt_chr_free_y <- biparental_GWAS_tidy %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(Chr), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_tidy$Chr), 
                     label = unique(biparental_GWAS_tidy$Chr)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free") 

biparental_GWAS_alt_chr_free_y

ggsave("biparental_GWAS_alt_chr_free_y.png",
       plot = biparental_GWAS_alt_chr_free_y,
       dpi = 1200,
       width = 10,
       height = 6)

Individual Manhattan plots for each alkaloid

kable(head(biparental_GWAS_raw))
MARKER Chr Phy_Posbp Ch_Mb Gen_PoscM Phy_Chr cM Dehydrotomatidine_neg_log10_pval Tomatidine_neg_log10_pval Dehydrotomatine_neg_log10_pval Alpha_Tomatine_neg_log10_pval Hydroxytomatine_neg_log10_pval Acetoxytomatine_neg_log10_pval DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval LycoperosideF_G_Esculeoside_A_neg_log10_pval Esculeoside_B_neg_log10_pval Total_neg_log10_pval
solcap_snp_sl_60078 1 2955601 1.029556 NA SL2.40ch01 NA 0.0215763 0.2061754 0.1746312 0.0288758 0.2954108 0.1550308 0.3127265 0.3675994 0.1304851 0.0710951
solcap_snp_sl_59771 1 6229659 1.062297 NA SL2.40ch01 NA 0.1346021 0.1159026 0.1746312 0.0288758 0.4247042 0.1711228 0.7267946 0.4875734 0.5098057 0.0710951
solcap_snp_sl_13762 1 67855513 1.678555 41.666899999999998 SL2.40ch01 0.320514615 0.0285136 0.1318688 0.1746312 0.0288758 0.2954108 0.1550308 0.2028721 0.1981261 0.5098057 0.0710951
solcap_snp_sl_43632 1 77935539 1.779355 NA SL2.40ch01 NA 0.1346021 0.0922492 0.1746312 0.1194513 0.9613055 0.1748867 0.7267946 1.2055482 0.4823208 0.2056115
solcap_snp_sl_13404 1 87977912 1.879779 120.8956 SL2.40ch01 0.92996615399999993 0.1346021 0.9355302 0.2823912 0.2449025 0.2954108 0.3269503 0.1497593 0.2099169 0.1304851 0.2056115
solcap_snp_sl_7410 2 2309824 2.023098 NA SL2.40ch02 NA 0.1346021 0.1159026 0.2707129 0.0816134 0.2954108 0.3269503 0.2028721 0.3463221 0.1304851 0.0710951
biparental_GWAS_manhattan_plots <- function(g){
biparental_GWAS_raw %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), color = as.factor(Chr))) +
  geom_point(aes_(y = as.name(g)), size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_raw$Chr), 
                     label = unique(biparental_GWAS_raw$Chr)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  labs(title = paste(g),
       x ="Chromosome",
       y = (expression("FDR corrected "-log[10](P))))
}  

all_biparental_gwas_manhatan_plots <- lapply(names(biparental_GWAS_raw[,8:ncol(biparental_GWAS_raw)]), 
                                            biparental_GWAS_manhattan_plots)
all_biparental_gwas_manhatan_plots
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Correlation Analysis

We are interested to understand how the concentration of each alkaloid is related to every other alkaloid. To do this, we will conduct a correlation analysis.

The inputs to the correlation analyses are the LS means per genotype.

head(Alkaloids_LSmeans)
## # A tibble: 6 x 37
##   Genotype Plot_Source Species Class Origin Provence Blanca_Cluster1
##   <chr>    <chr>       <chr>   <chr> <chr>  <chr>    <chr>          
## 1 CULBPT_… 2K9-8584    Proces… Cult… USA    NY       SLL_processing…
## 2 CULBPT_… 2K9-8622    Proces… Cult… USA    NY       SLL_processing…
## 3 CULBPT_… 2K17-7708-1 Proces… Cult… USA    NY       SLL_processing…
## 4 CULBPT0… 2K9-8566    Proces… Cult… USA    NY       SLL_processing…
## 5 E6203    2K9-8600    Proces… Cult… USA    CA       SLL_processing…
## 6 F06-2041 2K16-9843   Proces… Cult… USA    OH       SLL_processing…
## # … with 30 more variables: Blanca_Cluster2 <chr>, Passport_Species <chr>,
## #   Passport_Classification <chr>, Sim_Grouping <chr>, Dehydrotomatidine <dbl>,
## #   Tomatidine <dbl>, DehydrotomatineA1 <dbl>, Dehydrotomatine2 <dbl>,
## #   TotalDehydrotomatine <dbl>, Tomatine <dbl>, Hydroxytomatine1 <dbl>,
## #   Hydroxytomatine2 <dbl>, Hydroxytomatine3 <dbl>, Hydroxytomatine4 <dbl>,
## #   TotalHydroxytomatine <dbl>, Acetoxytomatine1 <dbl>, Acetoxytomatine2 <dbl>,
## #   Acetoxytomatine3 <dbl>, TotalAcetoxytomatine <dbl>,
## #   DehydrolycoperosideFGdehydroesculeosideA <dbl>,
## #   LycoperosideFGEsculeosideA1 <dbl>, LycoperosideFGEsculeosideA2 <dbl>,
## #   TotalLycoperosideFGEsculeosideA <dbl>, EsculeosideB1 <dbl>,
## #   EsculeosideB2 <dbl>, EsculeosideB3 <dbl>, TotalEsculeosideB <dbl>,
## #   Total <dbl>, Latitude <chr>, Longitude <chr>
# this is already trimmed of some meta-data we don't need
head(Alkaloids_LSmeans_forPCA)
## # A tibble: 6 x 12
##   Genotype Class Dehydrotomatidi… Tomatidine TotalDehydrotom… Tomatine
##   <chr>    <chr>            <dbl>      <dbl>            <dbl>    <dbl>
## 1 CULBPT_… Cult…        0.000150   0.00435            0.00615  0.193  
## 2 CULBPT_… Cult…       -0.0000272 -0.000172           0.00865  0.282  
## 3 CULBPT_… Cult…       -0.0000649 -0.000252           0.00852  0.176  
## 4 CULBPT0… Cult…       -0.0000272  0.000126          -0.00387 -0.00614
## 5 E6203    Cult…       -0.0000272  0.0000159          0.00995  0.293  
## 6 F06-2041 Cult…       -0.0000272 -0.000165          -0.00112  0.0170 
## # … with 6 more variables: TotalHydroxytomatine <dbl>,
## #   TotalAcetoxytomatine <dbl>, DehydrolycoperosideFGdehydroesculeosideA <dbl>,
## #   TotalLycoperosideFGEsculeosideA <dbl>, TotalEsculeosideB <dbl>, Total <dbl>
# all isomers
Alkaloids_LSmeans_allisomers_corr <- Alkaloids_LSmeans %>%
  select(Genotype, Class, Dehydrotomatidine:Total)

# sums only
Alkaloids_LSmeans_sums_corr <- Alkaloids_LSmeans_forPCA %>%
  select(Genotype, Class, Dehydrotomatidine:Total)

On sums

Full population

Alkaloids_sums_corr_matrix <- cor(Alkaloids_LSmeans_sums_corr[,3:ncol(Alkaloids_LSmeans_sums_corr)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix)
Dehydrotomatidine Tomatidine TotalDehydrotomatine Tomatine TotalHydroxytomatine TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA TotalLycoperosideFGEsculeosideA TotalEsculeosideB Total
Dehydrotomatidine 1.0000000 0.1958081 0.0800031 0.0856258 0.1463775 0.0700602 0.0660130 0.1257124 0.1973726 0.1778680
Tomatidine 0.1958081 1.0000000 0.5633672 0.5340258 0.1611836 0.0524480 0.0320171 0.0419116 0.0459817 0.2898556
TotalDehydrotomatine 0.0800031 0.5633672 1.0000000 0.8982311 0.2841456 0.4243220 -0.1007251 -0.1368764 -0.1708610 0.6085030
Tomatine 0.0856258 0.5340258 0.8982311 1.0000000 0.1063700 0.1017999 -0.1348184 -0.1594540 -0.1648607 0.3740744
TotalHydroxytomatine 0.1463775 0.1611836 0.2841456 0.1063700 1.0000000 0.4772983 0.0445803 0.0275158 0.0021843 0.5670118
TotalAcetoxytomatine 0.0700602 0.0524480 0.4243220 0.1017999 0.4772983 1.0000000 -0.1037893 -0.1228584 -0.1388088 0.7587569
DehydrolycoperosideFGdehydroesculeosideA 0.0660130 0.0320171 -0.1007251 -0.1348184 0.0445803 -0.1037893 1.0000000 0.9389721 0.7943348 0.4024231
TotalLycoperosideFGEsculeosideA 0.1257124 0.0419116 -0.1368764 -0.1594540 0.0275158 -0.1228584 0.9389721 1.0000000 0.8974951 0.4104711
TotalEsculeosideB 0.1973726 0.0459817 -0.1708610 -0.1648607 0.0021843 -0.1388088 0.7943348 0.8974951 1.0000000 0.3413784
Total 0.1778680 0.2898556 0.6085030 0.3740744 0.5670118 0.7587569 0.4024231 0.4104711 0.3413784 1.0000000
# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest <- cor.mtest(Alkaloids_sums_corr_matrix, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix <- sums_cor_mtest$p

Prettying up the axis labels

axis_labels_totals <- c("Dehydrotomatidine",
                 "Tomatidine",
                 "Dehydrotomatine",
                 "Alpha-Tomatine",
                 "Hydroxytomatidine",
                 "Acetoxytomatidine",
                 "Dehydrolycoperoside F/G/Dehydroesculeoside A",
                 "Lycoperoside F/G/Esculeoside A",
                 "Escueloside B",
                 "Total SteroidalAlkaloids")

colnames(Alkaloids_sums_corr_matrix) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix) <- axis_labels_totals %>% str_wrap(width = 24)

Create corrplot

corrplot_sum_fullpop <- corrplot(Alkaloids_sums_corr_matrix,
         method = "circle",
         p.mat = sums_pval_matrix,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col="black", # color of axis text
         title = "Full Population") 

corrplot_sum_fullpop
##                                                Dehydrotomatidine Tomatidine
## Dehydrotomatidine                                     1.00000000 0.19580805
## Tomatidine                                            0.19580805 1.00000000
## Dehydrotomatine                                       0.08000305 0.56336719
## Alpha-Tomatine                                        0.08562579 0.53402579
## Hydroxytomatidine                                     0.14637746 0.16118361
## Acetoxytomatidine                                     0.07006019 0.05244799
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        0.06601296 0.03201708
## Lycoperoside F/G/\nEsculeoside A                      0.12571240 0.04191165
## Escueloside B                                         0.19737260 0.04598168
## Total SteroidalAlkaloids                              0.17786796 0.28985563
##                                                Dehydrotomatine Alpha-Tomatine
## Dehydrotomatidine                                   0.08000305     0.08562579
## Tomatidine                                          0.56336719     0.53402579
## Dehydrotomatine                                     1.00000000     0.89823113
## Alpha-Tomatine                                      0.89823113     1.00000000
## Hydroxytomatidine                                   0.28414556     0.10637004
## Acetoxytomatidine                                   0.42432200     0.10179991
## Dehydrolycoperoside F/G/\nDehydroesculeoside A     -0.10072509    -0.13481845
## Lycoperoside F/G/\nEsculeoside A                   -0.13687639    -0.15945398
## Escueloside B                                      -0.17086098    -0.16486072
## Total SteroidalAlkaloids                            0.60850304     0.37407436
##                                                Hydroxytomatidine
## Dehydrotomatidine                                    0.146377464
## Tomatidine                                           0.161183612
## Dehydrotomatine                                      0.284145556
## Alpha-Tomatine                                       0.106370043
## Hydroxytomatidine                                    1.000000000
## Acetoxytomatidine                                    0.477298283
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       0.044580274
## Lycoperoside F/G/\nEsculeoside A                     0.027515791
## Escueloside B                                        0.002184304
## Total SteroidalAlkaloids                             0.567011840
##                                                Acetoxytomatidine
## Dehydrotomatidine                                     0.07006019
## Tomatidine                                            0.05244799
## Dehydrotomatine                                       0.42432200
## Alpha-Tomatine                                        0.10179991
## Hydroxytomatidine                                     0.47729828
## Acetoxytomatidine                                     1.00000000
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       -0.10378930
## Lycoperoside F/G/\nEsculeoside A                     -0.12285842
## Escueloside B                                        -0.13880876
## Total SteroidalAlkaloids                              0.75875692
##                                                Dehydrolycoperoside F/G/\nDehydroesculeoside A
## Dehydrotomatidine                                                                  0.06601296
## Tomatidine                                                                         0.03201708
## Dehydrotomatine                                                                   -0.10072509
## Alpha-Tomatine                                                                    -0.13481845
## Hydroxytomatidine                                                                  0.04458027
## Acetoxytomatidine                                                                 -0.10378930
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                                     1.00000000
## Lycoperoside F/G/\nEsculeoside A                                                   0.93897211
## Escueloside B                                                                      0.79433475
## Total SteroidalAlkaloids                                                           0.40242306
##                                                Lycoperoside F/G/\nEsculeoside A
## Dehydrotomatidine                                                    0.12571240
## Tomatidine                                                           0.04191165
## Dehydrotomatine                                                     -0.13687639
## Alpha-Tomatine                                                      -0.15945398
## Hydroxytomatidine                                                    0.02751579
## Acetoxytomatidine                                                   -0.12285842
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                       0.93897211
## Lycoperoside F/G/\nEsculeoside A                                     1.00000000
## Escueloside B                                                        0.89749512
## Total SteroidalAlkaloids                                             0.41047113
##                                                Escueloside B
## Dehydrotomatidine                                0.197372597
## Tomatidine                                       0.045981680
## Dehydrotomatine                                 -0.170860976
## Alpha-Tomatine                                  -0.164860723
## Hydroxytomatidine                                0.002184304
## Acetoxytomatidine                               -0.138808756
## Dehydrolycoperoside F/G/\nDehydroesculeoside A   0.794334751
## Lycoperoside F/G/\nEsculeoside A                 0.897495116
## Escueloside B                                    1.000000000
## Total SteroidalAlkaloids                         0.341378371
##                                                Total SteroidalAlkaloids
## Dehydrotomatidine                                             0.1778680
## Tomatidine                                                    0.2898556
## Dehydrotomatine                                               0.6085030
## Alpha-Tomatine                                                0.3740744
## Hydroxytomatidine                                             0.5670118
## Acetoxytomatidine                                             0.7587569
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                0.4024231
## Lycoperoside F/G/\nEsculeoside A                              0.4104711
## Escueloside B                                                 0.3413784
## Total SteroidalAlkaloids                                      1.0000000

Cultivated Processing

Alkaloids_LSmeans_sums_corr_cultproc <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Cultivated Processing")

head(Alkaloids_LSmeans_sums_corr_cultproc)
## # A tibble: 6 x 12
##   Genotype Class Dehydrotomatidi… Tomatidine TotalDehydrotom… Tomatine
##   <chr>    <chr>            <dbl>      <dbl>            <dbl>    <dbl>
## 1 CULBPT_… Cult…        0.000150   0.00435            0.00615  0.193  
## 2 CULBPT_… Cult…       -0.0000272 -0.000172           0.00865  0.282  
## 3 CULBPT_… Cult…       -0.0000649 -0.000252           0.00852  0.176  
## 4 CULBPT0… Cult…       -0.0000272  0.000126          -0.00387 -0.00614
## 5 E6203    Cult…       -0.0000272  0.0000159          0.00995  0.293  
## 6 F06-2041 Cult…       -0.0000272 -0.000165          -0.00112  0.0170 
## # … with 6 more variables: TotalHydroxytomatine <dbl>,
## #   TotalAcetoxytomatine <dbl>, DehydrolycoperosideFGdehydroesculeosideA <dbl>,
## #   TotalLycoperosideFGEsculeosideA <dbl>, TotalEsculeosideB <dbl>, Total <dbl>
Alkaloids_sums_corr_matrix_cultproc <- cor(Alkaloids_LSmeans_sums_corr_cultproc[,3:ncol(Alkaloids_LSmeans_sums_corr_cultproc)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_cultproc)
Dehydrotomatidine Tomatidine TotalDehydrotomatine Tomatine TotalHydroxytomatine TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA TotalLycoperosideFGEsculeosideA TotalEsculeosideB Total
Dehydrotomatidine 1.0000000 0.4684205 -0.1264388 -0.1813027 -0.0416145 -0.2197497 -0.1416577 -0.1963224 -0.1450557 -0.2113658
Tomatidine 0.4684205 1.0000000 -0.1667087 -0.0642947 0.4433877 -0.6276822 -0.7019078 -0.2838328 -0.4355407 -0.2204046
TotalDehydrotomatine -0.1264388 -0.1667087 1.0000000 0.9289992 0.4131378 0.6357718 0.4620495 0.7170347 0.5355441 0.9015676
Tomatine -0.1813027 -0.0642947 0.9289992 1.0000000 0.4899490 0.5716836 0.2772481 0.5985033 0.3644665 0.8552038
TotalHydroxytomatine -0.0416145 0.4433877 0.4131378 0.4899490 1.0000000 -0.2258674 -0.3151714 0.4508818 0.0261019 0.5243611
TotalAcetoxytomatine -0.2197497 -0.6276822 0.6357718 0.5716836 -0.2258674 1.0000000 0.8374333 0.4971272 0.5903021 0.6082227
DehydrolycoperosideFGdehydroesculeosideA -0.1416577 -0.7019078 0.4620495 0.2772481 -0.3151714 0.8374333 1.0000000 0.6382313 0.7746924 0.5540462
TotalLycoperosideFGEsculeosideA -0.1963224 -0.2838328 0.7170347 0.5985033 0.4508818 0.4971272 0.6382313 1.0000000 0.7581278 0.9244855
TotalEsculeosideB -0.1450557 -0.4355407 0.5355441 0.3644665 0.0261019 0.5903021 0.7746924 0.7581278 1.0000000 0.6777464
Total -0.2113658 -0.2204046 0.9015676 0.8552038 0.5243611 0.6082227 0.5540462 0.9244855 0.6777464 1.0000000
# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_cultproc <- cor.mtest(Alkaloids_sums_corr_matrix_cultproc, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_cultproc <- sums_cor_mtest_cultproc$p

colnames(Alkaloids_sums_corr_matrix_cultproc) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_cultproc) <- axis_labels_totals %>% str_wrap(width = 24)

Create corrplot

corrplot_sum_cultproc <- corrplot(Alkaloids_sums_corr_matrix_cultproc,
         method = "circle",
         p.mat = sums_pval_matrix_cultproc,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Cultivated Processing") 

corrplot_sum_cultproc
##                                                Dehydrotomatidine  Tomatidine
## Dehydrotomatidine                                     1.00000000  0.46842045
## Tomatidine                                            0.46842045  1.00000000
## Dehydrotomatine                                      -0.12643880 -0.16670867
## Alpha-Tomatine                                       -0.18130266 -0.06429471
## Hydroxytomatidine                                    -0.04161454  0.44338766
## Acetoxytomatidine                                    -0.21974974 -0.62768221
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       -0.14165773 -0.70190780
## Lycoperoside F/G/\nEsculeoside A                     -0.19632237 -0.28383284
## Escueloside B                                        -0.14505574 -0.43554073
## Total SteroidalAlkaloids                             -0.21136578 -0.22040458
##                                                Dehydrotomatine Alpha-Tomatine
## Dehydrotomatidine                                   -0.1264388    -0.18130266
## Tomatidine                                          -0.1667087    -0.06429471
## Dehydrotomatine                                      1.0000000     0.92899921
## Alpha-Tomatine                                       0.9289992     1.00000000
## Hydroxytomatidine                                    0.4131378     0.48994899
## Acetoxytomatidine                                    0.6357718     0.57168360
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       0.4620495     0.27724810
## Lycoperoside F/G/\nEsculeoside A                     0.7170347     0.59850327
## Escueloside B                                        0.5355441     0.36446652
## Total SteroidalAlkaloids                             0.9015676     0.85520381
##                                                Hydroxytomatidine
## Dehydrotomatidine                                    -0.04161454
## Tomatidine                                            0.44338766
## Dehydrotomatine                                       0.41313777
## Alpha-Tomatine                                        0.48994899
## Hydroxytomatidine                                     1.00000000
## Acetoxytomatidine                                    -0.22586735
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       -0.31517144
## Lycoperoside F/G/\nEsculeoside A                      0.45088181
## Escueloside B                                         0.02610185
## Total SteroidalAlkaloids                              0.52436112
##                                                Acetoxytomatidine
## Dehydrotomatidine                                     -0.2197497
## Tomatidine                                            -0.6276822
## Dehydrotomatine                                        0.6357718
## Alpha-Tomatine                                         0.5716836
## Hydroxytomatidine                                     -0.2258674
## Acetoxytomatidine                                      1.0000000
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         0.8374333
## Lycoperoside F/G/\nEsculeoside A                       0.4971272
## Escueloside B                                          0.5903021
## Total SteroidalAlkaloids                               0.6082227
##                                                Dehydrolycoperoside F/G/\nDehydroesculeoside A
## Dehydrotomatidine                                                                  -0.1416577
## Tomatidine                                                                         -0.7019078
## Dehydrotomatine                                                                     0.4620495
## Alpha-Tomatine                                                                      0.2772481
## Hydroxytomatidine                                                                  -0.3151714
## Acetoxytomatidine                                                                   0.8374333
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                                      1.0000000
## Lycoperoside F/G/\nEsculeoside A                                                    0.6382313
## Escueloside B                                                                       0.7746924
## Total SteroidalAlkaloids                                                            0.5540462
##                                                Lycoperoside F/G/\nEsculeoside A
## Dehydrotomatidine                                                    -0.1963224
## Tomatidine                                                           -0.2838328
## Dehydrotomatine                                                       0.7170347
## Alpha-Tomatine                                                        0.5985033
## Hydroxytomatidine                                                     0.4508818
## Acetoxytomatidine                                                     0.4971272
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                        0.6382313
## Lycoperoside F/G/\nEsculeoside A                                      1.0000000
## Escueloside B                                                         0.7581278
## Total SteroidalAlkaloids                                              0.9244855
##                                                Escueloside B
## Dehydrotomatidine                                -0.14505574
## Tomatidine                                       -0.43554073
## Dehydrotomatine                                   0.53554407
## Alpha-Tomatine                                    0.36446652
## Hydroxytomatidine                                 0.02610185
## Acetoxytomatidine                                 0.59030213
## Dehydrolycoperoside F/G/\nDehydroesculeoside A    0.77469236
## Lycoperoside F/G/\nEsculeoside A                  0.75812782
## Escueloside B                                     1.00000000
## Total SteroidalAlkaloids                          0.67774645
##                                                Total SteroidalAlkaloids
## Dehydrotomatidine                                            -0.2113658
## Tomatidine                                                   -0.2204046
## Dehydrotomatine                                               0.9015676
## Alpha-Tomatine                                                0.8552038
## Hydroxytomatidine                                             0.5243611
## Acetoxytomatidine                                             0.6082227
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                0.5540462
## Lycoperoside F/G/\nEsculeoside A                              0.9244855
## Escueloside B                                                 0.6777464
## Total SteroidalAlkaloids                                      1.0000000

Cultivated Cherry

Alkaloids_LSmeans_sums_corr_cultcherry <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Cultivated Cherry")

head(Alkaloids_LSmeans_sums_corr_cultcherry)
## # A tibble: 6 x 12
##   Genotype Class Dehydrotomatidi… Tomatidine TotalDehydrotom… Tomatine
##   <chr>    <chr>            <dbl>      <dbl>            <dbl>    <dbl>
## 1 Gold Ba… Cult…      -0.0000272  -0.0000886         -0.00779  -0.115 
## 2 NC2C     Cult…      -0.0000272  -0.0000291          0.0254    0.348 
## 3 OR11     Cult…       0.000933    0.00178           -0.00336  -0.0191
## 4 Princip… Cult…      -0.00000245  0.000198           0.0369    0.552 
## 5 Tainan   Cult…      -0.0000272  -0.000165           0.0519    0.439 
## 6 VFNT Ch… Cult…       0.0000451   0.00267            0.0237    0.509 
## # … with 6 more variables: TotalHydroxytomatine <dbl>,
## #   TotalAcetoxytomatine <dbl>, DehydrolycoperosideFGdehydroesculeosideA <dbl>,
## #   TotalLycoperosideFGEsculeosideA <dbl>, TotalEsculeosideB <dbl>, Total <dbl>
Alkaloids_sums_corr_matrix_cultcherry <- cor(Alkaloids_LSmeans_sums_corr_cultcherry[,3:ncol(Alkaloids_LSmeans_sums_corr_cultcherry)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_cultcherry)
Dehydrotomatidine Tomatidine TotalDehydrotomatine Tomatine TotalHydroxytomatine TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA TotalLycoperosideFGEsculeosideA TotalEsculeosideB Total
Dehydrotomatidine 1.0000000 0.4919742 -0.5167106 -0.4948699 -0.5302301 -0.4550694 -0.3754987 -0.5016173 -0.5425818 -0.5231387
Tomatidine 0.4919742 1.0000000 -0.2606036 0.0677192 -0.1611858 -0.0777542 -0.4705056 -0.4475548 -0.4560050 -0.3867608
TotalDehydrotomatine -0.5167106 -0.2606036 1.0000000 0.8827630 0.9633704 0.9819199 0.5292670 0.9397001 0.7712606 0.9671888
Tomatine -0.4948699 0.0677192 0.8827630 1.0000000 0.9559698 0.9182527 0.4163367 0.7415641 0.7689791 0.8163084
TotalHydroxytomatine -0.5302301 -0.1611858 0.9633704 0.9559698 1.0000000 0.9562286 0.4278644 0.8427625 0.8073498 0.8997845
TotalAcetoxytomatine -0.4550694 -0.0777542 0.9819199 0.9182527 0.9562286 1.0000000 0.4615349 0.8875976 0.6959946 0.9253314
DehydrolycoperosideFGdehydroesculeosideA -0.3754987 -0.4705056 0.5292670 0.4163367 0.4278644 0.4615349 1.0000000 0.7583459 0.7662476 0.7228368
TotalLycoperosideFGEsculeosideA -0.5016173 -0.4475548 0.9397001 0.7415641 0.8427625 0.8875976 0.7583459 1.0000000 0.8155910 0.9924880
TotalEsculeosideB -0.5425818 -0.4560050 0.7712606 0.7689791 0.8073498 0.6959946 0.7662476 0.8155910 1.0000000 0.8467805
Total -0.5231387 -0.3867608 0.9671888 0.8163084 0.8997845 0.9253314 0.7228368 0.9924880 0.8467805 1.0000000
# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_cultcherry <- cor.mtest(Alkaloids_sums_corr_matrix_cultcherry, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_cultcherry <- sums_cor_mtest_cultcherry$p

colnames(Alkaloids_sums_corr_matrix_cultcherry) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_cultcherry) <- axis_labels_totals %>% str_wrap(width = 24)

Create corrplot

corrplot_sum_cultcherry <- corrplot(Alkaloids_sums_corr_matrix_cultcherry,
         method = "circle",
         p.mat = sums_pval_matrix_cultcherry,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Cultivated Cherry") 

corrplot_sum_cultcherry
##                                                Dehydrotomatidine  Tomatidine
## Dehydrotomatidine                                      1.0000000  0.49197420
## Tomatidine                                             0.4919742  1.00000000
## Dehydrotomatine                                       -0.5167106 -0.26060360
## Alpha-Tomatine                                        -0.4948699  0.06771920
## Hydroxytomatidine                                     -0.5302301 -0.16118584
## Acetoxytomatidine                                     -0.4550694 -0.07775423
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        -0.3754987 -0.47050557
## Lycoperoside F/G/\nEsculeoside A                      -0.5016173 -0.44755485
## Escueloside B                                         -0.5425818 -0.45600496
## Total SteroidalAlkaloids                              -0.5231387 -0.38676082
##                                                Dehydrotomatine Alpha-Tomatine
## Dehydrotomatidine                                   -0.5167106     -0.4948699
## Tomatidine                                          -0.2606036      0.0677192
## Dehydrotomatine                                      1.0000000      0.8827630
## Alpha-Tomatine                                       0.8827630      1.0000000
## Hydroxytomatidine                                    0.9633704      0.9559698
## Acetoxytomatidine                                    0.9819199      0.9182527
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       0.5292670      0.4163367
## Lycoperoside F/G/\nEsculeoside A                     0.9397001      0.7415641
## Escueloside B                                        0.7712606      0.7689791
## Total SteroidalAlkaloids                             0.9671888      0.8163084
##                                                Hydroxytomatidine
## Dehydrotomatidine                                     -0.5302301
## Tomatidine                                            -0.1611858
## Dehydrotomatine                                        0.9633704
## Alpha-Tomatine                                         0.9559698
## Hydroxytomatidine                                      1.0000000
## Acetoxytomatidine                                      0.9562286
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         0.4278644
## Lycoperoside F/G/\nEsculeoside A                       0.8427625
## Escueloside B                                          0.8073498
## Total SteroidalAlkaloids                               0.8997845
##                                                Acetoxytomatidine
## Dehydrotomatidine                                    -0.45506939
## Tomatidine                                           -0.07775423
## Dehydrotomatine                                       0.98191993
## Alpha-Tomatine                                        0.91825269
## Hydroxytomatidine                                     0.95622863
## Acetoxytomatidine                                     1.00000000
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        0.46153485
## Lycoperoside F/G/\nEsculeoside A                      0.88759756
## Escueloside B                                         0.69599464
## Total SteroidalAlkaloids                              0.92533139
##                                                Dehydrolycoperoside F/G/\nDehydroesculeoside A
## Dehydrotomatidine                                                                  -0.3754987
## Tomatidine                                                                         -0.4705056
## Dehydrotomatine                                                                     0.5292670
## Alpha-Tomatine                                                                      0.4163367
## Hydroxytomatidine                                                                   0.4278644
## Acetoxytomatidine                                                                   0.4615349
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                                      1.0000000
## Lycoperoside F/G/\nEsculeoside A                                                    0.7583459
## Escueloside B                                                                       0.7662476
## Total SteroidalAlkaloids                                                            0.7228368
##                                                Lycoperoside F/G/\nEsculeoside A
## Dehydrotomatidine                                                    -0.5016173
## Tomatidine                                                           -0.4475548
## Dehydrotomatine                                                       0.9397001
## Alpha-Tomatine                                                        0.7415641
## Hydroxytomatidine                                                     0.8427625
## Acetoxytomatidine                                                     0.8875976
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                        0.7583459
## Lycoperoside F/G/\nEsculeoside A                                      1.0000000
## Escueloside B                                                         0.8155910
## Total SteroidalAlkaloids                                              0.9924880
##                                                Escueloside B
## Dehydrotomatidine                                 -0.5425818
## Tomatidine                                        -0.4560050
## Dehydrotomatine                                    0.7712606
## Alpha-Tomatine                                     0.7689791
## Hydroxytomatidine                                  0.8073498
## Acetoxytomatidine                                  0.6959946
## Dehydrolycoperoside F/G/\nDehydroesculeoside A     0.7662476
## Lycoperoside F/G/\nEsculeoside A                   0.8155910
## Escueloside B                                      1.0000000
## Total SteroidalAlkaloids                           0.8467805
##                                                Total SteroidalAlkaloids
## Dehydrotomatidine                                            -0.5231387
## Tomatidine                                                   -0.3867608
## Dehydrotomatine                                               0.9671888
## Alpha-Tomatine                                                0.8163084
## Hydroxytomatidine                                             0.8997845
## Acetoxytomatidine                                             0.9253314
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                0.7228368
## Lycoperoside F/G/\nEsculeoside A                              0.9924880
## Escueloside B                                                 0.8467805
## Total SteroidalAlkaloids                                      1.0000000

Wide Cross Hybrid

Alkaloids_LSmeans_sums_corr_hybrid <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Wide Cross Hybrid")

head(Alkaloids_LSmeans_sums_corr_hybrid)
## # A tibble: 6 x 12
##   Genotype Class Dehydrotomatidi… Tomatidine TotalDehydrotom… Tomatine
##   <chr>    <chr>            <dbl>      <dbl>            <dbl>    <dbl>
## 1 FG16-511 Wide…        0.000507     1.96e-3          0.00121  -0.0106
## 2 FG16-513 Wide…        0.0000961    3.89e-3          0.0519    0.771 
## 3 FG16-515 Wide…       -0.0000272   -2.58e-4          0.0127    0.210 
## 4 FG16-517 Wide…       -0.0000272   -5.20e-5          0.0673    0.974 
## 5 FG16-519 Wide…        0.0000148   -9.82e-6          0.0222    0.290 
## 6 FG16-521 Wide…       -0.0000272    4.80e-3          0.0524    0.405 
## # … with 6 more variables: TotalHydroxytomatine <dbl>,
## #   TotalAcetoxytomatine <dbl>, DehydrolycoperosideFGdehydroesculeosideA <dbl>,
## #   TotalLycoperosideFGEsculeosideA <dbl>, TotalEsculeosideB <dbl>, Total <dbl>
Alkaloids_sums_corr_matrix_hybrid <- cor(Alkaloids_LSmeans_sums_corr_hybrid[,3:ncol(Alkaloids_LSmeans_sums_corr_hybrid)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_hybrid)
Dehydrotomatidine Tomatidine TotalDehydrotomatine Tomatine TotalHydroxytomatine TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA TotalLycoperosideFGEsculeosideA TotalEsculeosideB Total
Dehydrotomatidine 1.0000000 0.1397014 -0.5893117 -0.5344343 -0.2147759 -0.6632852 0.7878836 0.2668297 0.0943624 0.1025021
Tomatidine 0.1397014 1.0000000 0.3158726 0.0664688 0.7721989 0.3694851 0.1775846 0.3485307 -0.2488696 0.3588483
TotalDehydrotomatine -0.5893117 0.3158726 1.0000000 0.9249773 0.7340561 0.8120865 -0.3194375 0.5099942 0.2345872 0.6417176
Tomatine -0.5344343 0.0664688 0.9249773 1.0000000 0.4502890 0.5629749 -0.4346433 0.3993385 0.1642010 0.5161662
TotalHydroxytomatine -0.2147759 0.7721989 0.7340561 0.4502890 1.0000000 0.8294138 0.1703831 0.6755995 0.2742662 0.7488283
TotalAcetoxytomatine -0.6632852 0.3694851 0.8120865 0.5629749 0.8294138 1.0000000 -0.1566234 0.4558879 0.3751345 0.5872678
DehydrolycoperosideFGdehydroesculeosideA 0.7878836 0.1775846 -0.3194375 -0.4346433 0.1703831 -0.1566234 1.0000000 0.6273648 0.5988857 0.5030305
TotalLycoperosideFGEsculeosideA 0.2668297 0.3485307 0.5099942 0.3993385 0.6755995 0.4558879 0.6273648 1.0000000 0.7486009 0.9852850
TotalEsculeosideB 0.0943624 -0.2488696 0.2345872 0.1642010 0.2742662 0.3751345 0.5988857 0.7486009 1.0000000 0.7357394
Total 0.1025021 0.3588483 0.6417176 0.5161662 0.7488283 0.5872678 0.5030305 0.9852850 0.7357394 1.0000000
# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_hybrid <- cor.mtest(Alkaloids_sums_corr_matrix_hybrid, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_hybrid <- sums_cor_mtest_hybrid$p

colnames(Alkaloids_sums_corr_matrix_hybrid) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_hybrid) <- axis_labels_totals %>% str_wrap(width = 24)

Create corrplot

corrplot_sum_hybrid <- corrplot(Alkaloids_sums_corr_matrix_hybrid,
         method = "circle",
         p.mat = sums_pval_matrix_hybrid,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Wide Cross Hybrid") 

corrplot_sum_hybrid
##                                                Dehydrotomatidine  Tomatidine
## Dehydrotomatidine                                     1.00000000  0.13970142
## Tomatidine                                            0.13970142  1.00000000
## Dehydrotomatine                                      -0.58931174  0.31587258
## Alpha-Tomatine                                       -0.53443425  0.06646882
## Hydroxytomatidine                                    -0.21477593  0.77219888
## Acetoxytomatidine                                    -0.66328522  0.36948508
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        0.78788362  0.17758464
## Lycoperoside F/G/\nEsculeoside A                      0.26682968  0.34853067
## Escueloside B                                         0.09436237 -0.24886956
## Total SteroidalAlkaloids                              0.10250211  0.35884828
##                                                Dehydrotomatine Alpha-Tomatine
## Dehydrotomatidine                                   -0.5893117    -0.53443425
## Tomatidine                                           0.3158726     0.06646882
## Dehydrotomatine                                      1.0000000     0.92497732
## Alpha-Tomatine                                       0.9249773     1.00000000
## Hydroxytomatidine                                    0.7340561     0.45028899
## Acetoxytomatidine                                    0.8120865     0.56297490
## Dehydrolycoperoside F/G/\nDehydroesculeoside A      -0.3194375    -0.43464328
## Lycoperoside F/G/\nEsculeoside A                     0.5099942     0.39933852
## Escueloside B                                        0.2345872     0.16420099
## Total SteroidalAlkaloids                             0.6417176     0.51616624
##                                                Hydroxytomatidine
## Dehydrotomatidine                                     -0.2147759
## Tomatidine                                             0.7721989
## Dehydrotomatine                                        0.7340561
## Alpha-Tomatine                                         0.4502890
## Hydroxytomatidine                                      1.0000000
## Acetoxytomatidine                                      0.8294138
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         0.1703831
## Lycoperoside F/G/\nEsculeoside A                       0.6755995
## Escueloside B                                          0.2742662
## Total SteroidalAlkaloids                               0.7488283
##                                                Acetoxytomatidine
## Dehydrotomatidine                                     -0.6632852
## Tomatidine                                             0.3694851
## Dehydrotomatine                                        0.8120865
## Alpha-Tomatine                                         0.5629749
## Hydroxytomatidine                                      0.8294138
## Acetoxytomatidine                                      1.0000000
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        -0.1566234
## Lycoperoside F/G/\nEsculeoside A                       0.4558879
## Escueloside B                                          0.3751345
## Total SteroidalAlkaloids                               0.5872678
##                                                Dehydrolycoperoside F/G/\nDehydroesculeoside A
## Dehydrotomatidine                                                                   0.7878836
## Tomatidine                                                                          0.1775846
## Dehydrotomatine                                                                    -0.3194375
## Alpha-Tomatine                                                                     -0.4346433
## Hydroxytomatidine                                                                   0.1703831
## Acetoxytomatidine                                                                  -0.1566234
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                                      1.0000000
## Lycoperoside F/G/\nEsculeoside A                                                    0.6273648
## Escueloside B                                                                       0.5988857
## Total SteroidalAlkaloids                                                            0.5030305
##                                                Lycoperoside F/G/\nEsculeoside A
## Dehydrotomatidine                                                     0.2668297
## Tomatidine                                                            0.3485307
## Dehydrotomatine                                                       0.5099942
## Alpha-Tomatine                                                        0.3993385
## Hydroxytomatidine                                                     0.6755995
## Acetoxytomatidine                                                     0.4558879
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                        0.6273648
## Lycoperoside F/G/\nEsculeoside A                                      1.0000000
## Escueloside B                                                         0.7486009
## Total SteroidalAlkaloids                                              0.9852850
##                                                Escueloside B
## Dehydrotomatidine                                 0.09436237
## Tomatidine                                       -0.24886956
## Dehydrotomatine                                   0.23458717
## Alpha-Tomatine                                    0.16420099
## Hydroxytomatidine                                 0.27426621
## Acetoxytomatidine                                 0.37513451
## Dehydrolycoperoside F/G/\nDehydroesculeoside A    0.59888569
## Lycoperoside F/G/\nEsculeoside A                  0.74860094
## Escueloside B                                     1.00000000
## Total SteroidalAlkaloids                          0.73573942
##                                                Total SteroidalAlkaloids
## Dehydrotomatidine                                             0.1025021
## Tomatidine                                                    0.3588483
## Dehydrotomatine                                               0.6417176
## Alpha-Tomatine                                                0.5161662
## Hydroxytomatidine                                             0.7488283
## Acetoxytomatidine                                             0.5872678
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                0.5030305
## Lycoperoside F/G/\nEsculeoside A                              0.9852850
## Escueloside B                                                 0.7357394
## Total SteroidalAlkaloids                                      1.0000000

Wild Cherry

Alkaloids_LSmeans_sums_corr_wildcherry <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Wild Cherry")

head(Alkaloids_LSmeans_sums_corr_wildcherry)
## # A tibble: 6 x 12
##   Genotype Class Dehydrotomatidi… Tomatidine TotalDehydrotom… Tomatine
##   <chr>    <chr>            <dbl>      <dbl>            <dbl>    <dbl>
## 1 LA1314   Wild…      0.0000947     0.00285            0.115     1.04 
## 2 LA1338   Wild…      0.000756      0.0298             0.183     2.01 
## 3 LA1464   Wild…      0.0000961     0.00397            0.225     1.94 
## 4 LA1512   Wild…     -0.0000111     0.00244            0.0166    0.196
## 5 LA1542   Wild…      0.0000344     0.0147             0.101     1.19 
## 6 LA1549   Wild…     -0.000000590   0.000120           0.0581    0.169
## # … with 6 more variables: TotalHydroxytomatine <dbl>,
## #   TotalAcetoxytomatine <dbl>, DehydrolycoperosideFGdehydroesculeosideA <dbl>,
## #   TotalLycoperosideFGEsculeosideA <dbl>, TotalEsculeosideB <dbl>, Total <dbl>
Alkaloids_sums_corr_matrix_wildcherry <- cor(Alkaloids_LSmeans_sums_corr_wildcherry[,3:ncol(Alkaloids_LSmeans_sums_corr_wildcherry)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_wildcherry)
Dehydrotomatidine Tomatidine TotalDehydrotomatine Tomatine TotalHydroxytomatine TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA TotalLycoperosideFGEsculeosideA TotalEsculeosideB Total
Dehydrotomatidine 1.0000000 0.1653183 0.0809563 0.1030620 0.1820587 0.0983976 -0.1708305 -0.0682854 0.2253353 0.1779811
Tomatidine 0.1653183 1.0000000 0.5683517 0.5268467 0.0554669 -0.0139936 -0.3208071 -0.3313171 -0.2306840 0.1500363
TotalDehydrotomatine 0.0809563 0.5683517 1.0000000 0.8935611 0.0738361 0.3051755 -0.5881332 -0.6409702 -0.5363556 0.5425229
Tomatine 0.1030620 0.5268467 0.8935611 1.0000000 -0.0686327 -0.0255713 -0.4746418 -0.5057519 -0.4120394 0.2859454
TotalHydroxytomatine 0.1820587 0.0554669 0.0738361 -0.0686327 1.0000000 0.3925270 -0.3112422 -0.3440016 -0.2829034 0.4607150
TotalAcetoxytomatine 0.0983976 -0.0139936 0.3051755 -0.0255713 0.3925270 1.0000000 -0.4326207 -0.4734814 -0.3834099 0.8931440
DehydrolycoperosideFGdehydroesculeosideA -0.1708305 -0.3208071 -0.5881332 -0.4746418 -0.3112422 -0.4326207 1.0000000 0.8900323 0.6923486 -0.3908422
TotalLycoperosideFGEsculeosideA -0.0682854 -0.3313171 -0.6409702 -0.5057519 -0.3440016 -0.4734814 0.8900323 1.0000000 0.8423234 -0.4103081
TotalEsculeosideB 0.2253353 -0.2306840 -0.5363556 -0.4120394 -0.2829034 -0.3834099 0.6923486 0.8423234 1.0000000 -0.3132890
Total 0.1779811 0.1500363 0.5425229 0.2859454 0.4607150 0.8931440 -0.3908422 -0.4103081 -0.3132890 1.0000000
# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_wildcherry <- cor.mtest(Alkaloids_sums_corr_matrix_wildcherry, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_wildcherry <- sums_cor_mtest_wildcherry$p

colnames(Alkaloids_sums_corr_matrix_wildcherry) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_wildcherry) <- axis_labels_totals %>% str_wrap(width = 24)

Create corrplot

corrplot_sum_wildcherry <- corrplot(Alkaloids_sums_corr_matrix_wildcherry,
         method = "circle",
         p.mat = sums_pval_matrix_wildcherry,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Wild Cherry") 

corrplot_sum_wildcherry
##                                                Dehydrotomatidine  Tomatidine
## Dehydrotomatidine                                     1.00000000  0.16531831
## Tomatidine                                            0.16531831  1.00000000
## Dehydrotomatine                                       0.08095631  0.56835168
## Alpha-Tomatine                                        0.10306196  0.52684670
## Hydroxytomatidine                                     0.18205873  0.05546689
## Acetoxytomatidine                                     0.09839757 -0.01399359
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       -0.17083053 -0.32080708
## Lycoperoside F/G/\nEsculeoside A                     -0.06828543 -0.33131714
## Escueloside B                                         0.22533530 -0.23068400
## Total SteroidalAlkaloids                              0.17798110  0.15003628
##                                                Dehydrotomatine Alpha-Tomatine
## Dehydrotomatidine                                   0.08095631     0.10306196
## Tomatidine                                          0.56835168     0.52684670
## Dehydrotomatine                                     1.00000000     0.89356113
## Alpha-Tomatine                                      0.89356113     1.00000000
## Hydroxytomatidine                                   0.07383606    -0.06863275
## Acetoxytomatidine                                   0.30517548    -0.02557127
## Dehydrolycoperoside F/G/\nDehydroesculeoside A     -0.58813319    -0.47464183
## Lycoperoside F/G/\nEsculeoside A                   -0.64097016    -0.50575195
## Escueloside B                                      -0.53635564    -0.41203939
## Total SteroidalAlkaloids                            0.54252288     0.28594543
##                                                Hydroxytomatidine
## Dehydrotomatidine                                     0.18205873
## Tomatidine                                            0.05546689
## Dehydrotomatine                                       0.07383606
## Alpha-Tomatine                                       -0.06863275
## Hydroxytomatidine                                     1.00000000
## Acetoxytomatidine                                     0.39252700
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       -0.31124225
## Lycoperoside F/G/\nEsculeoside A                     -0.34400158
## Escueloside B                                        -0.28290340
## Total SteroidalAlkaloids                              0.46071504
##                                                Acetoxytomatidine
## Dehydrotomatidine                                     0.09839757
## Tomatidine                                           -0.01399359
## Dehydrotomatine                                       0.30517548
## Alpha-Tomatine                                       -0.02557127
## Hydroxytomatidine                                     0.39252700
## Acetoxytomatidine                                     1.00000000
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       -0.43262069
## Lycoperoside F/G/\nEsculeoside A                     -0.47348143
## Escueloside B                                        -0.38340995
## Total SteroidalAlkaloids                              0.89314401
##                                                Dehydrolycoperoside F/G/\nDehydroesculeoside A
## Dehydrotomatidine                                                                  -0.1708305
## Tomatidine                                                                         -0.3208071
## Dehydrotomatine                                                                    -0.5881332
## Alpha-Tomatine                                                                     -0.4746418
## Hydroxytomatidine                                                                  -0.3112422
## Acetoxytomatidine                                                                  -0.4326207
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                                      1.0000000
## Lycoperoside F/G/\nEsculeoside A                                                    0.8900323
## Escueloside B                                                                       0.6923486
## Total SteroidalAlkaloids                                                           -0.3908422
##                                                Lycoperoside F/G/\nEsculeoside A
## Dehydrotomatidine                                                   -0.06828543
## Tomatidine                                                          -0.33131714
## Dehydrotomatine                                                     -0.64097016
## Alpha-Tomatine                                                      -0.50575195
## Hydroxytomatidine                                                   -0.34400158
## Acetoxytomatidine                                                   -0.47348143
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                       0.89003226
## Lycoperoside F/G/\nEsculeoside A                                     1.00000000
## Escueloside B                                                        0.84232341
## Total SteroidalAlkaloids                                            -0.41030805
##                                                Escueloside B
## Dehydrotomatidine                                  0.2253353
## Tomatidine                                        -0.2306840
## Dehydrotomatine                                   -0.5363556
## Alpha-Tomatine                                    -0.4120394
## Hydroxytomatidine                                 -0.2829034
## Acetoxytomatidine                                 -0.3834099
## Dehydrolycoperoside F/G/\nDehydroesculeoside A     0.6923486
## Lycoperoside F/G/\nEsculeoside A                   0.8423234
## Escueloside B                                      1.0000000
## Total SteroidalAlkaloids                          -0.3132890
##                                                Total SteroidalAlkaloids
## Dehydrotomatidine                                             0.1779811
## Tomatidine                                                    0.1500363
## Dehydrotomatine                                               0.5425229
## Alpha-Tomatine                                                0.2859454
## Hydroxytomatidine                                             0.4607150
## Acetoxytomatidine                                             0.8931440
## Dehydrolycoperoside F/G/\nDehydroesculeoside A               -0.3908422
## Lycoperoside F/G/\nEsculeoside A                             -0.4103081
## Escueloside B                                                -0.3132890
## Total SteroidalAlkaloids                                      1.0000000

S. pimpinellifolium

Alkaloids_LSmeans_sums_corr_pimp <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "S. pimpinellifolium")

head(Alkaloids_LSmeans_sums_corr_pimp)
## # A tibble: 6 x 12
##   Genotype Class Dehydrotomatidi… Tomatidine TotalDehydrotom… Tomatine
##   <chr>    <chr>            <dbl>      <dbl>            <dbl>    <dbl>
## 1 LA0373   S. p…       0.00000677    0.0157            0.0164   0.448 
## 2 LA0400   S. p…       0.0000788     0.00221           0.127    1.09  
## 3 LA0411   S. p…       0.00388       0.0148            0.0122   0.0702
## 4 LA0722   S. p…       0.0000675     0.0132            0.0286   0.823 
## 5 LA1237   S. p…       0.0000433     0.00550           0.0275   0.202 
## 6 LA1261   S. p…       0.000193      0.00223           0.0722   0.602 
## # … with 6 more variables: TotalHydroxytomatine <dbl>,
## #   TotalAcetoxytomatine <dbl>, DehydrolycoperosideFGdehydroesculeosideA <dbl>,
## #   TotalLycoperosideFGEsculeosideA <dbl>, TotalEsculeosideB <dbl>, Total <dbl>
Alkaloids_sums_corr_matrix_pimp <- 
  cor(Alkaloids_LSmeans_sums_corr_pimp[,3:ncol(Alkaloids_LSmeans_sums_corr_pimp)], 
      method = "pearson")

kable(Alkaloids_sums_corr_matrix_pimp)
Dehydrotomatidine Tomatidine TotalDehydrotomatine Tomatine TotalHydroxytomatine TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA TotalLycoperosideFGEsculeosideA TotalEsculeosideB Total
Dehydrotomatidine 1.0000000 0.1954352 -0.1589147 -0.1786063 -0.2006380 -0.1042024 -0.1943138 -0.1920589 -0.2610654 -0.2107423
Tomatidine 0.1954352 1.0000000 0.1127297 0.3890450 0.1091375 -0.1196598 0.2248365 0.3521061 0.2745544 0.1713881
TotalDehydrotomatine -0.1589147 0.1127297 1.0000000 0.8873039 0.7349537 0.7276673 0.3562835 0.3485428 -0.0967109 0.7596013
Tomatine -0.1786063 0.3890450 0.8873039 1.0000000 0.6434961 0.4733321 0.5100770 0.5057092 0.1105995 0.7012005
TotalHydroxytomatine -0.2006380 0.1091375 0.7349537 0.6434961 1.0000000 0.7724928 0.4573555 0.4838539 0.1788664 0.8755287
TotalAcetoxytomatine -0.1042024 -0.1196598 0.7276673 0.4733321 0.7724928 1.0000000 0.0444011 0.0822551 -0.1014073 0.7461320
DehydrolycoperosideFGdehydroesculeosideA -0.1943138 0.2248365 0.3562835 0.5100770 0.4573555 0.0444011 1.0000000 0.8983434 0.4450893 0.6346404
TotalLycoperosideFGEsculeosideA -0.1920589 0.3521061 0.3485428 0.5057092 0.4838539 0.0822551 0.8983434 1.0000000 0.6270869 0.7219874
TotalEsculeosideB -0.2610654 0.2745544 -0.0967109 0.1105995 0.1788664 -0.1014073 0.4450893 0.6270869 1.0000000 0.3526253
Total -0.2107423 0.1713881 0.7596013 0.7012005 0.8755287 0.7461320 0.6346404 0.7219874 0.3526253 1.0000000
# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_pimp <- cor.mtest(Alkaloids_sums_corr_matrix_pimp, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_pimp <- sums_cor_mtest_pimp$p

colnames(Alkaloids_sums_corr_matrix_pimp) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_pimp) <- axis_labels_totals %>% str_wrap(width = 24)

Create corrplot

corrplot_sum_pimp <- corrplot(Alkaloids_sums_corr_matrix_pimp,
         method = "circle",
         p.mat = sums_pval_matrix_pimp,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "S. pimpinellifolium") 

corrplot_sum_pimp
##                                                Dehydrotomatidine Tomatidine
## Dehydrotomatidine                                      1.0000000  0.1954352
## Tomatidine                                             0.1954352  1.0000000
## Dehydrotomatine                                       -0.1589147  0.1127297
## Alpha-Tomatine                                        -0.1786063  0.3890450
## Hydroxytomatidine                                     -0.2006380  0.1091375
## Acetoxytomatidine                                     -0.1042024 -0.1196598
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        -0.1943138  0.2248365
## Lycoperoside F/G/\nEsculeoside A                      -0.1920589  0.3521061
## Escueloside B                                         -0.2610654  0.2745544
## Total SteroidalAlkaloids                              -0.2107423  0.1713881
##                                                Dehydrotomatine Alpha-Tomatine
## Dehydrotomatidine                                   -0.1589147     -0.1786063
## Tomatidine                                           0.1127297      0.3890450
## Dehydrotomatine                                      1.0000000      0.8873039
## Alpha-Tomatine                                       0.8873039      1.0000000
## Hydroxytomatidine                                    0.7349537      0.6434961
## Acetoxytomatidine                                    0.7276673      0.4733321
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       0.3562835      0.5100770
## Lycoperoside F/G/\nEsculeoside A                     0.3485428      0.5057092
## Escueloside B                                       -0.0967109      0.1105995
## Total SteroidalAlkaloids                             0.7596013      0.7012005
##                                                Hydroxytomatidine
## Dehydrotomatidine                                     -0.2006380
## Tomatidine                                             0.1091375
## Dehydrotomatine                                        0.7349537
## Alpha-Tomatine                                         0.6434961
## Hydroxytomatidine                                      1.0000000
## Acetoxytomatidine                                      0.7724928
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         0.4573555
## Lycoperoside F/G/\nEsculeoside A                       0.4838539
## Escueloside B                                          0.1788664
## Total SteroidalAlkaloids                               0.8755287
##                                                Acetoxytomatidine
## Dehydrotomatidine                                    -0.10420242
## Tomatidine                                           -0.11965980
## Dehydrotomatine                                       0.72766735
## Alpha-Tomatine                                        0.47333209
## Hydroxytomatidine                                     0.77249278
## Acetoxytomatidine                                     1.00000000
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        0.04440112
## Lycoperoside F/G/\nEsculeoside A                      0.08225513
## Escueloside B                                        -0.10140730
## Total SteroidalAlkaloids                              0.74613201
##                                                Dehydrolycoperoside F/G/\nDehydroesculeoside A
## Dehydrotomatidine                                                                 -0.19431383
## Tomatidine                                                                         0.22483653
## Dehydrotomatine                                                                    0.35628355
## Alpha-Tomatine                                                                     0.51007703
## Hydroxytomatidine                                                                  0.45735554
## Acetoxytomatidine                                                                  0.04440112
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                                     1.00000000
## Lycoperoside F/G/\nEsculeoside A                                                   0.89834342
## Escueloside B                                                                      0.44508934
## Total SteroidalAlkaloids                                                           0.63464045
##                                                Lycoperoside F/G/\nEsculeoside A
## Dehydrotomatidine                                                   -0.19205889
## Tomatidine                                                           0.35210614
## Dehydrotomatine                                                      0.34854280
## Alpha-Tomatine                                                       0.50570923
## Hydroxytomatidine                                                    0.48385388
## Acetoxytomatidine                                                    0.08225513
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                       0.89834342
## Lycoperoside F/G/\nEsculeoside A                                     1.00000000
## Escueloside B                                                        0.62708687
## Total SteroidalAlkaloids                                             0.72198744
##                                                Escueloside B
## Dehydrotomatidine                                 -0.2610654
## Tomatidine                                         0.2745544
## Dehydrotomatine                                   -0.0967109
## Alpha-Tomatine                                     0.1105995
## Hydroxytomatidine                                  0.1788664
## Acetoxytomatidine                                 -0.1014073
## Dehydrolycoperoside F/G/\nDehydroesculeoside A     0.4450893
## Lycoperoside F/G/\nEsculeoside A                   0.6270869
## Escueloside B                                      1.0000000
## Total SteroidalAlkaloids                           0.3526253
##                                                Total SteroidalAlkaloids
## Dehydrotomatidine                                            -0.2107423
## Tomatidine                                                    0.1713881
## Dehydrotomatine                                               0.7596013
## Alpha-Tomatine                                                0.7012005
## Hydroxytomatidine                                             0.8755287
## Acetoxytomatidine                                             0.7461320
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                0.6346404
## Lycoperoside F/G/\nEsculeoside A                              0.7219874
## Escueloside B                                                 0.3526253
## Total SteroidalAlkaloids                                      1.0000000

On all isomers

Alkaloids_allisomers_corr_matrix <- cor(Alkaloids_LSmeans_allisomers_corr[,3:ncol(Alkaloids_LSmeans_allisomers_corr)], 
                                  method = "pearson")

kable(Alkaloids_allisomers_corr_matrix)
Dehydrotomatidine Tomatidine DehydrotomatineA1 Dehydrotomatine2 TotalDehydrotomatine Tomatine Hydroxytomatine1 Hydroxytomatine2 Hydroxytomatine3 Hydroxytomatine4 TotalHydroxytomatine Acetoxytomatine1 Acetoxytomatine2 Acetoxytomatine3 TotalAcetoxytomatine DehydrolycoperosideFGdehydroesculeosideA LycoperosideFGEsculeosideA1 LycoperosideFGEsculeosideA2 TotalLycoperosideFGEsculeosideA EsculeosideB1 EsculeosideB2 EsculeosideB3 TotalEsculeosideB Total
Dehydrotomatidine 1.0000000 0.1958081 0.0724118 0.0705199 0.0800031 0.0856258 0.1091838 0.1248884 0.1239814 0.1096175 0.1463775 0.0640569 0.0706559 0.0534588 0.0700602 0.0660130 0.1444286 0.1236331 0.1257124 0.2328156 0.2119572 0.1693180 0.1973726 0.1778680
Tomatidine 0.1958081 1.0000000 0.5860685 0.0789416 0.5633672 0.5340258 0.1158963 0.1122517 0.1424779 0.1469736 0.1611836 0.0461304 0.0496523 0.1361824 0.0524480 0.0320171 0.0572057 0.0403493 0.0419116 0.0323270 0.0410946 0.0545631 0.0459817 0.2898556
DehydrotomatineA1 0.0724118 0.5860685 1.0000000 0.2818199 0.9864355 0.9382021 0.0331233 0.2394422 0.1221723 0.3065069 0.2166712 0.2916249 0.2646223 0.3766102 0.2702264 -0.0866433 -0.1185372 -0.1217159 -0.1216872 -0.1685617 -0.1668972 -0.1430407 -0.1555893 0.5112946
Dehydrotomatine2 0.0705199 0.0789416 0.2818199 1.0000000 0.4354934 0.1051570 -0.0109944 0.8013982 0.1148049 0.7467082 0.4726344 0.9773513 0.9985487 0.9151758 0.9982811 -0.1135963 -0.1139857 -0.1342242 -0.1327240 -0.1607342 -0.1437631 -0.1334054 -0.1454483 0.7528223
TotalDehydrotomatine 0.0800031 0.5633672 0.9864355 0.4354934 1.0000000 0.8982311 0.0291960 0.3617561 0.1342658 0.4153210 0.2841456 0.4408177 0.4191099 0.5099155 0.4243220 -0.1007251 -0.1307151 -0.1371600 -0.1368764 -0.1856471 -0.1811820 -0.1570272 -0.1708610 0.6085030
Tomatine 0.0856258 0.5340258 0.9382021 0.1051570 0.8982311 1.0000000 -0.0015510 0.0765479 0.0809241 0.1481210 0.1063700 0.1063834 0.0970628 0.2223864 0.1017999 -0.1348184 -0.1493455 -0.1600657 -0.1594540 -0.1734284 -0.1635850 -0.1567053 -0.1648607 0.3740744
Hydroxytomatine1 0.1091838 0.1158963 0.0331233 -0.0109944 0.0291960 -0.0015510 1.0000000 0.2069522 0.9654454 0.5488552 0.8378693 0.0000567 -0.0138284 -0.0171833 -0.0133994 0.0703478 0.0816724 0.0720805 0.0730705 0.0553339 0.0569084 0.0726778 0.0658671 0.1778832
Hydroxytomatine2 0.1248884 0.1122517 0.2394422 0.8013982 0.3617561 0.0765479 0.2069522 1.0000000 0.2700617 0.8561187 0.6642823 0.8519328 0.8091936 0.8803927 0.8159471 0.2751955 0.2767988 0.2701918 0.2713272 0.1885897 0.1732515 0.2347645 0.2154994 0.8699286
Hydroxytomatine3 0.1239814 0.1424779 0.1221723 0.1148049 0.1342658 0.0809241 0.9654454 0.2700617 1.0000000 0.6385122 0.8969458 0.1017829 0.1127336 0.1320528 0.1132673 -0.0697310 -0.0753197 -0.0864902 -0.0856876 -0.0918256 -0.0831342 -0.0807035 -0.0857253 0.2204869
Hydroxytomatine4 0.1096175 0.1469736 0.3065069 0.7467082 0.4153210 0.1481210 0.5488552 0.8561187 0.6385122 1.0000000 0.9009765 0.7553073 0.7488537 0.7921486 0.7529894 0.0165520 -0.0029602 -0.0117509 -0.0110035 -0.0688381 -0.0713697 -0.0333669 -0.0495522 0.7380949
TotalHydroxytomatine 0.1463775 0.1611836 0.2166712 0.4726344 0.2841456 0.1063700 0.8378693 0.6642823 0.8969458 0.9009765 1.0000000 0.4817615 0.4740211 0.5171793 0.4772983 0.0445803 0.0378604 0.0264609 0.0275158 -0.0143059 -0.0138350 0.0148815 0.0021843 0.5670118
Acetoxytomatine1 0.0640569 0.0461304 0.2916249 0.9773513 0.4408177 0.1063834 0.0000567 0.8519328 0.1017829 0.7553073 0.4817615 1.0000000 0.9794336 0.9035689 0.9810958 0.0161189 -0.0066834 -0.0088030 -0.0086351 -0.0853359 -0.0856655 -0.0497913 -0.0659978 0.8103828
Acetoxytomatine2 0.0706559 0.0496523 0.2646223 0.9985487 0.4191099 0.0970628 -0.0138284 0.8091936 0.1127336 0.7488537 0.4740211 0.9794336 1.0000000 0.9203234 0.9998823 -0.1097445 -0.1099837 -0.1298928 -0.1284126 -0.1574876 -0.1410966 -0.1302652 -0.1422758 0.7532965
Acetoxytomatine3 0.0534588 0.1361824 0.3766102 0.9151758 0.5099155 0.2223864 -0.0171833 0.8803927 0.1320528 0.7921486 0.5171793 0.9035689 0.9203234 1.0000000 0.9252208 -0.0774208 -0.0847509 -0.0998221 -0.0987046 -0.1351296 -0.1243172 -0.1064610 -0.1193145 0.7712523
TotalAcetoxytomatine 0.0700602 0.0524480 0.2702264 0.9982811 0.4243220 0.1017999 -0.0133994 0.8159471 0.1132673 0.7529894 0.4772983 0.9810958 0.9998823 0.9252208 1.0000000 -0.1037893 -0.1052035 -0.1242768 -0.1228584 -0.1542633 -0.1386987 -0.1265554 -0.1388088 0.7587569
DehydrolycoperosideFGdehydroesculeosideA 0.0660130 0.0320171 -0.0866433 -0.1135963 -0.1007251 -0.1348184 0.0703478 0.2751955 -0.0697310 0.0165520 0.0445803 0.0161189 -0.1097445 -0.0774208 -0.1037893 1.0000000 0.8984623 0.9407488 0.9389721 0.7499837 0.6492439 0.8289141 0.7943348 0.4024231
LycoperosideFGEsculeosideA1 0.1444286 0.0572057 -0.1185372 -0.1139857 -0.1307151 -0.1493455 0.0816724 0.2767988 -0.0753197 -0.0029602 0.0378604 -0.0066834 -0.1099837 -0.0847509 -0.1052035 0.8984623 1.0000000 0.9743469 0.9786019 0.8933985 0.8299445 0.9528679 0.9310481 0.4179792
LycoperosideFGEsculeosideA2 0.1236331 0.0403493 -0.1217159 -0.1342242 -0.1371600 -0.1600657 0.0720805 0.2701918 -0.0864902 -0.0117509 0.0264609 -0.0088030 -0.1298928 -0.0998221 -0.1242768 0.9407488 0.9743469 1.0000000 0.9998049 0.8573587 0.7710545 0.9157869 0.8922558 0.4088272
TotalLycoperosideFGEsculeosideA 0.1257124 0.0419116 -0.1216872 -0.1327240 -0.1368764 -0.1594540 0.0730705 0.2713272 -0.0856876 -0.0110035 0.0275158 -0.0086351 -0.1284126 -0.0987046 -0.1228584 0.9389721 0.9786019 0.9998049 1.0000000 0.8622847 0.7778085 0.9209244 0.8974951 0.4104711
EsculeosideB1 0.2328156 0.0323270 -0.1685617 -0.1607342 -0.1856471 -0.1734284 0.0553339 0.1885897 -0.0918256 -0.0688381 -0.0143059 -0.0853359 -0.1574876 -0.1351296 -0.1542633 0.7499837 0.8933985 0.8573587 0.8622847 1.0000000 0.9752093 0.9694447 0.9905730 0.3055147
EsculeosideB2 0.2119572 0.0410946 -0.1668972 -0.1437631 -0.1811820 -0.1635850 0.0569084 0.1732515 -0.0831342 -0.0713697 -0.0138350 -0.0856655 -0.1410966 -0.1243172 -0.1386987 0.6492439 0.8299445 0.7710545 0.7778085 0.9752093 1.0000000 0.9343539 0.9635943 0.2748309
EsculeosideB3 0.1693180 0.0545631 -0.1430407 -0.1334054 -0.1570272 -0.1567053 0.0726778 0.2347645 -0.0807035 -0.0333669 0.0148815 -0.0497913 -0.1302652 -0.1064610 -0.1265554 0.8289141 0.9528679 0.9157869 0.9209244 0.9694447 0.9343539 1.0000000 0.9936469 0.3675321
TotalEsculeosideB 0.1973726 0.0459817 -0.1555893 -0.1454483 -0.1708610 -0.1648607 0.0658671 0.2154994 -0.0857253 -0.0495522 0.0021843 -0.0659978 -0.1422758 -0.1193145 -0.1388088 0.7943348 0.9310481 0.8922558 0.8974951 0.9905730 0.9635943 0.9936469 1.0000000 0.3413784
Total 0.1778680 0.2898556 0.5112946 0.7528223 0.6085030 0.3740744 0.1778832 0.8699286 0.2204869 0.7380949 0.5670118 0.8103828 0.7532965 0.7712523 0.7587569 0.4024231 0.4179792 0.4088272 0.4104711 0.3055147 0.2748309 0.3675321 0.3413784 1.0000000
# run cor.mtest to get pvalues and CI for each pair of inputs
allisomers_cor_mtest <- cor.mtest(Alkaloids_allisomers_corr_matrix, 
                            conf.level = 0.95)

# save the pvalue matrix
allisomers_pval_matrix <- allisomers_cor_mtest$p

Prettying up the axis labels

axis_labels_allisomers <- c("Dehydrotomatidine",
                 "Tomatidine",
                 "Dehydrotomatine 1",
                 "Dehydrotomatidine 2",
                 "Total Dehydrotomatidine",
                 "Alpha-Tomatine",
                 "Hydroxytomatidine 1",
                 "Hydroxytomatidine 2",
                 "Hydroxytomatidine 3",
                 "Hydroxytomatidine 4",
                 "Total Hydroxytomatidine",
                 "Acetoxytomatidine 1",
                 "Acetoxytomatidine 2",
                 "Acetoxytomatidine 3",
                 "Total Acetoxytomatidine",
                 "Dehydrolycoperoside F/G/Dehydroesculeoside A",
                 "Lycoperoside F/G/Esculeoside A 1",
                 "Lycoperoside F/G/Esculeoside A 2",
                 "Total Lycoperoside F/G/Esculeoside A",
                 "Escueloside B 1",
                 "Escueloside B 2",
                 "Escueloside B 3",
                 "Total Escueloside B",
                 "Total SteroidalAlkaloids")

colnames(Alkaloids_allisomers_corr_matrix) <- axis_labels_allisomers %>% str_wrap(width = 24)
rownames(Alkaloids_allisomers_corr_matrix) <- axis_labels_allisomers %>% str_wrap(width = 24)

Create corrplot

corrplot_allisomers_fullpop <- corrplot(Alkaloids_allisomers_corr_matrix,
         method = "circle",
         p.mat = allisomers_pval_matrix,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 0.5, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 0.5, # size of axis text
         tl.col="black", # color of axis text
         title = "Full Population") 

corrplot_allisomers_fullpop
##                                                Dehydrotomatidine Tomatidine
## Dehydrotomatidine                                     1.00000000 0.19580805
## Tomatidine                                            0.19580805 1.00000000
## Dehydrotomatine 1                                     0.07241178 0.58606853
## Dehydrotomatidine 2                                   0.07051987 0.07894160
## Total Dehydrotomatidine                               0.08000305 0.56336719
## Alpha-Tomatine                                        0.08562579 0.53402579
## Hydroxytomatidine 1                                   0.10918383 0.11589626
## Hydroxytomatidine 2                                   0.12488837 0.11225170
## Hydroxytomatidine 3                                   0.12398135 0.14247792
## Hydroxytomatidine 4                                   0.10961754 0.14697361
## Total Hydroxytomatidine                               0.14637746 0.16118361
## Acetoxytomatidine 1                                   0.06405692 0.04613035
## Acetoxytomatidine 2                                   0.07065590 0.04965232
## Acetoxytomatidine 3                                   0.05345881 0.13618239
## Total Acetoxytomatidine                               0.07006019 0.05244799
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        0.06601296 0.03201708
## Lycoperoside F/G/\nEsculeoside A 1                    0.14442857 0.05720569
## Lycoperoside F/G/\nEsculeoside A 2                    0.12363309 0.04034930
## Total Lycoperoside F/G/\nEsculeoside A                0.12571240 0.04191165
## Escueloside B 1                                       0.23281563 0.03232704
## Escueloside B 2                                       0.21195717 0.04109465
## Escueloside B 3                                       0.16931799 0.05456313
## Total Escueloside B                                   0.19737260 0.04598168
## Total SteroidalAlkaloids                              0.17786796 0.28985563
##                                                Dehydrotomatine 1
## Dehydrotomatidine                                     0.07241178
## Tomatidine                                            0.58606853
## Dehydrotomatine 1                                     1.00000000
## Dehydrotomatidine 2                                   0.28181994
## Total Dehydrotomatidine                               0.98643545
## Alpha-Tomatine                                        0.93820206
## Hydroxytomatidine 1                                   0.03312334
## Hydroxytomatidine 2                                   0.23944223
## Hydroxytomatidine 3                                   0.12217225
## Hydroxytomatidine 4                                   0.30650687
## Total Hydroxytomatidine                               0.21667116
## Acetoxytomatidine 1                                   0.29162490
## Acetoxytomatidine 2                                   0.26462231
## Acetoxytomatidine 3                                   0.37661020
## Total Acetoxytomatidine                               0.27022641
## Dehydrolycoperoside F/G/\nDehydroesculeoside A       -0.08664335
## Lycoperoside F/G/\nEsculeoside A 1                   -0.11853716
## Lycoperoside F/G/\nEsculeoside A 2                   -0.12171588
## Total Lycoperoside F/G/\nEsculeoside A               -0.12168719
## Escueloside B 1                                      -0.16856173
## Escueloside B 2                                      -0.16689724
## Escueloside B 3                                      -0.14304066
## Total Escueloside B                                  -0.15558932
## Total SteroidalAlkaloids                              0.51129461
##                                                Dehydrotomatidine 2
## Dehydrotomatidine                                       0.07051987
## Tomatidine                                              0.07894160
## Dehydrotomatine 1                                       0.28181994
## Dehydrotomatidine 2                                     1.00000000
## Total Dehydrotomatidine                                 0.43549336
## Alpha-Tomatine                                          0.10515696
## Hydroxytomatidine 1                                    -0.01099438
## Hydroxytomatidine 2                                     0.80139816
## Hydroxytomatidine 3                                     0.11480493
## Hydroxytomatidine 4                                     0.74670825
## Total Hydroxytomatidine                                 0.47263436
## Acetoxytomatidine 1                                     0.97735129
## Acetoxytomatidine 2                                     0.99854874
## Acetoxytomatidine 3                                     0.91517584
## Total Acetoxytomatidine                                 0.99828107
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         -0.11359627
## Lycoperoside F/G/\nEsculeoside A 1                     -0.11398571
## Lycoperoside F/G/\nEsculeoside A 2                     -0.13422417
## Total Lycoperoside F/G/\nEsculeoside A                 -0.13272398
## Escueloside B 1                                        -0.16073421
## Escueloside B 2                                        -0.14376312
## Escueloside B 3                                        -0.13340536
## Total Escueloside B                                    -0.14544825
## Total SteroidalAlkaloids                                0.75282232
##                                                Total Dehydrotomatidine
## Dehydrotomatidine                                           0.08000305
## Tomatidine                                                  0.56336719
## Dehydrotomatine 1                                           0.98643545
## Dehydrotomatidine 2                                         0.43549336
## Total Dehydrotomatidine                                     1.00000000
## Alpha-Tomatine                                              0.89823113
## Hydroxytomatidine 1                                         0.02919603
## Hydroxytomatidine 2                                         0.36175611
## Hydroxytomatidine 3                                         0.13426581
## Hydroxytomatidine 4                                         0.41532096
## Total Hydroxytomatidine                                     0.28414556
## Acetoxytomatidine 1                                         0.44081774
## Acetoxytomatidine 2                                         0.41910991
## Acetoxytomatidine 3                                         0.50991547
## Total Acetoxytomatidine                                     0.42432200
## Dehydrolycoperoside F/G/\nDehydroesculeoside A             -0.10072509
## Lycoperoside F/G/\nEsculeoside A 1                         -0.13071514
## Lycoperoside F/G/\nEsculeoside A 2                         -0.13715996
## Total Lycoperoside F/G/\nEsculeoside A                     -0.13687639
## Escueloside B 1                                            -0.18564715
## Escueloside B 2                                            -0.18118200
## Escueloside B 3                                            -0.15702722
## Total Escueloside B                                        -0.17086098
## Total SteroidalAlkaloids                                    0.60850304
##                                                Alpha-Tomatine
## Dehydrotomatidine                                  0.08562579
## Tomatidine                                         0.53402579
## Dehydrotomatine 1                                  0.93820206
## Dehydrotomatidine 2                                0.10515696
## Total Dehydrotomatidine                            0.89823113
## Alpha-Tomatine                                     1.00000000
## Hydroxytomatidine 1                               -0.00155102
## Hydroxytomatidine 2                                0.07654785
## Hydroxytomatidine 3                                0.08092407
## Hydroxytomatidine 4                                0.14812102
## Total Hydroxytomatidine                            0.10637004
## Acetoxytomatidine 1                                0.10638341
## Acetoxytomatidine 2                                0.09706283
## Acetoxytomatidine 3                                0.22238636
## Total Acetoxytomatidine                            0.10179991
## Dehydrolycoperoside F/G/\nDehydroesculeoside A    -0.13481845
## Lycoperoside F/G/\nEsculeoside A 1                -0.14934552
## Lycoperoside F/G/\nEsculeoside A 2                -0.16006567
## Total Lycoperoside F/G/\nEsculeoside A            -0.15945398
## Escueloside B 1                                   -0.17342845
## Escueloside B 2                                   -0.16358503
## Escueloside B 3                                   -0.15670526
## Total Escueloside B                               -0.16486072
## Total SteroidalAlkaloids                           0.37407436
##                                                Hydroxytomatidine 1
## Dehydrotomatidine                                     0.1091838282
## Tomatidine                                            0.1158962630
## Dehydrotomatine 1                                     0.0331233402
## Dehydrotomatidine 2                                  -0.0109943817
## Total Dehydrotomatidine                               0.0291960338
## Alpha-Tomatine                                       -0.0015510199
## Hydroxytomatidine 1                                   1.0000000000
## Hydroxytomatidine 2                                   0.2069521810
## Hydroxytomatidine 3                                   0.9654454421
## Hydroxytomatidine 4                                   0.5488552034
## Total Hydroxytomatidine                               0.8378693298
## Acetoxytomatidine 1                                   0.0000566979
## Acetoxytomatidine 2                                  -0.0138283591
## Acetoxytomatidine 3                                  -0.0171833228
## Total Acetoxytomatidine                              -0.0133993766
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        0.0703477918
## Lycoperoside F/G/\nEsculeoside A 1                    0.0816723804
## Lycoperoside F/G/\nEsculeoside A 2                    0.0720804629
## Total Lycoperoside F/G/\nEsculeoside A                0.0730705038
## Escueloside B 1                                       0.0553338696
## Escueloside B 2                                       0.0569084056
## Escueloside B 3                                       0.0726778021
## Total Escueloside B                                   0.0658670825
## Total SteroidalAlkaloids                              0.1778832251
##                                                Hydroxytomatidine 2
## Dehydrotomatidine                                       0.12488837
## Tomatidine                                              0.11225170
## Dehydrotomatine 1                                       0.23944223
## Dehydrotomatidine 2                                     0.80139816
## Total Dehydrotomatidine                                 0.36175611
## Alpha-Tomatine                                          0.07654785
## Hydroxytomatidine 1                                     0.20695218
## Hydroxytomatidine 2                                     1.00000000
## Hydroxytomatidine 3                                     0.27006174
## Hydroxytomatidine 4                                     0.85611874
## Total Hydroxytomatidine                                 0.66428234
## Acetoxytomatidine 1                                     0.85193284
## Acetoxytomatidine 2                                     0.80919362
## Acetoxytomatidine 3                                     0.88039274
## Total Acetoxytomatidine                                 0.81594708
## Dehydrolycoperoside F/G/\nDehydroesculeoside A          0.27519553
## Lycoperoside F/G/\nEsculeoside A 1                      0.27679880
## Lycoperoside F/G/\nEsculeoside A 2                      0.27019176
## Total Lycoperoside F/G/\nEsculeoside A                  0.27132722
## Escueloside B 1                                         0.18858965
## Escueloside B 2                                         0.17325154
## Escueloside B 3                                         0.23476450
## Total Escueloside B                                     0.21549940
## Total SteroidalAlkaloids                                0.86992862
##                                                Hydroxytomatidine 3
## Dehydrotomatidine                                       0.12398135
## Tomatidine                                              0.14247792
## Dehydrotomatine 1                                       0.12217225
## Dehydrotomatidine 2                                     0.11480493
## Total Dehydrotomatidine                                 0.13426581
## Alpha-Tomatine                                          0.08092407
## Hydroxytomatidine 1                                     0.96544544
## Hydroxytomatidine 2                                     0.27006174
## Hydroxytomatidine 3                                     1.00000000
## Hydroxytomatidine 4                                     0.63851222
## Total Hydroxytomatidine                                 0.89694577
## Acetoxytomatidine 1                                     0.10178287
## Acetoxytomatidine 2                                     0.11273364
## Acetoxytomatidine 3                                     0.13205282
## Total Acetoxytomatidine                                 0.11326732
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         -0.06973100
## Lycoperoside F/G/\nEsculeoside A 1                     -0.07531969
## Lycoperoside F/G/\nEsculeoside A 2                     -0.08649015
## Total Lycoperoside F/G/\nEsculeoside A                 -0.08568765
## Escueloside B 1                                        -0.09182562
## Escueloside B 2                                        -0.08313422
## Escueloside B 3                                        -0.08070351
## Total Escueloside B                                    -0.08572530
## Total SteroidalAlkaloids                                0.22048692
##                                                Hydroxytomatidine 4
## Dehydrotomatidine                                      0.109617539
## Tomatidine                                             0.146973607
## Dehydrotomatine 1                                      0.306506871
## Dehydrotomatidine 2                                    0.746708249
## Total Dehydrotomatidine                                0.415320955
## Alpha-Tomatine                                         0.148121024
## Hydroxytomatidine 1                                    0.548855203
## Hydroxytomatidine 2                                    0.856118744
## Hydroxytomatidine 3                                    0.638512218
## Hydroxytomatidine 4                                    1.000000000
## Total Hydroxytomatidine                                0.900976471
## Acetoxytomatidine 1                                    0.755307260
## Acetoxytomatidine 2                                    0.748853651
## Acetoxytomatidine 3                                    0.792148572
## Total Acetoxytomatidine                                0.752989380
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         0.016551986
## Lycoperoside F/G/\nEsculeoside A 1                    -0.002960218
## Lycoperoside F/G/\nEsculeoside A 2                    -0.011750855
## Total Lycoperoside F/G/\nEsculeoside A                -0.011003522
## Escueloside B 1                                       -0.068838132
## Escueloside B 2                                       -0.071369720
## Escueloside B 3                                       -0.033366923
## Total Escueloside B                                   -0.049552236
## Total SteroidalAlkaloids                               0.738094920
##                                                Total Hydroxytomatidine
## Dehydrotomatidine                                          0.146377464
## Tomatidine                                                 0.161183612
## Dehydrotomatine 1                                          0.216671156
## Dehydrotomatidine 2                                        0.472634361
## Total Dehydrotomatidine                                    0.284145556
## Alpha-Tomatine                                             0.106370043
## Hydroxytomatidine 1                                        0.837869330
## Hydroxytomatidine 2                                        0.664282337
## Hydroxytomatidine 3                                        0.896945769
## Hydroxytomatidine 4                                        0.900976471
## Total Hydroxytomatidine                                    1.000000000
## Acetoxytomatidine 1                                        0.481761484
## Acetoxytomatidine 2                                        0.474021078
## Acetoxytomatidine 3                                        0.517179337
## Total Acetoxytomatidine                                    0.477298283
## Dehydrolycoperoside F/G/\nDehydroesculeoside A             0.044580274
## Lycoperoside F/G/\nEsculeoside A 1                         0.037860385
## Lycoperoside F/G/\nEsculeoside A 2                         0.026460923
## Total Lycoperoside F/G/\nEsculeoside A                     0.027515791
## Escueloside B 1                                           -0.014305913
## Escueloside B 2                                           -0.013835024
## Escueloside B 3                                            0.014881543
## Total Escueloside B                                        0.002184304
## Total SteroidalAlkaloids                                   0.567011840
##                                                Acetoxytomatidine 1
## Dehydrotomatidine                                     0.0640569248
## Tomatidine                                            0.0461303509
## Dehydrotomatine 1                                     0.2916249004
## Dehydrotomatidine 2                                   0.9773512874
## Total Dehydrotomatidine                               0.4408177435
## Alpha-Tomatine                                        0.1063834072
## Hydroxytomatidine 1                                   0.0000566979
## Hydroxytomatidine 2                                   0.8519328365
## Hydroxytomatidine 3                                   0.1017828749
## Hydroxytomatidine 4                                   0.7553072601
## Total Hydroxytomatidine                               0.4817614837
## Acetoxytomatidine 1                                   1.0000000000
## Acetoxytomatidine 2                                   0.9794335917
## Acetoxytomatidine 3                                   0.9035688644
## Total Acetoxytomatidine                               0.9810957916
## Dehydrolycoperoside F/G/\nDehydroesculeoside A        0.0161189221
## Lycoperoside F/G/\nEsculeoside A 1                   -0.0066833751
## Lycoperoside F/G/\nEsculeoside A 2                   -0.0088029884
## Total Lycoperoside F/G/\nEsculeoside A               -0.0086350657
## Escueloside B 1                                      -0.0853359239
## Escueloside B 2                                      -0.0856655168
## Escueloside B 3                                      -0.0497912579
## Total Escueloside B                                  -0.0659978150
## Total SteroidalAlkaloids                              0.8103828276
##                                                Acetoxytomatidine 2
## Dehydrotomatidine                                       0.07065590
## Tomatidine                                              0.04965232
## Dehydrotomatine 1                                       0.26462231
## Dehydrotomatidine 2                                     0.99854874
## Total Dehydrotomatidine                                 0.41910991
## Alpha-Tomatine                                          0.09706283
## Hydroxytomatidine 1                                    -0.01382836
## Hydroxytomatidine 2                                     0.80919362
## Hydroxytomatidine 3                                     0.11273364
## Hydroxytomatidine 4                                     0.74885365
## Total Hydroxytomatidine                                 0.47402108
## Acetoxytomatidine 1                                     0.97943359
## Acetoxytomatidine 2                                     1.00000000
## Acetoxytomatidine 3                                     0.92032342
## Total Acetoxytomatidine                                 0.99988232
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         -0.10974450
## Lycoperoside F/G/\nEsculeoside A 1                     -0.10998373
## Lycoperoside F/G/\nEsculeoside A 2                     -0.12989283
## Total Lycoperoside F/G/\nEsculeoside A                 -0.12841264
## Escueloside B 1                                        -0.15748758
## Escueloside B 2                                        -0.14109664
## Escueloside B 3                                        -0.13026519
## Total Escueloside B                                    -0.14227579
## Total SteroidalAlkaloids                                0.75329651
##                                                Acetoxytomatidine 3
## Dehydrotomatidine                                       0.05345881
## Tomatidine                                              0.13618239
## Dehydrotomatine 1                                       0.37661020
## Dehydrotomatidine 2                                     0.91517584
## Total Dehydrotomatidine                                 0.50991547
## Alpha-Tomatine                                          0.22238636
## Hydroxytomatidine 1                                    -0.01718332
## Hydroxytomatidine 2                                     0.88039274
## Hydroxytomatidine 3                                     0.13205282
## Hydroxytomatidine 4                                     0.79214857
## Total Hydroxytomatidine                                 0.51717934
## Acetoxytomatidine 1                                     0.90356886
## Acetoxytomatidine 2                                     0.92032342
## Acetoxytomatidine 3                                     1.00000000
## Total Acetoxytomatidine                                 0.92522079
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         -0.07742075
## Lycoperoside F/G/\nEsculeoside A 1                     -0.08475092
## Lycoperoside F/G/\nEsculeoside A 2                     -0.09982207
## Total Lycoperoside F/G/\nEsculeoside A                 -0.09870464
## Escueloside B 1                                        -0.13512963
## Escueloside B 2                                        -0.12431715
## Escueloside B 3                                        -0.10646100
## Total Escueloside B                                    -0.11931451
## Total SteroidalAlkaloids                                0.77125232
##                                                Total Acetoxytomatidine
## Dehydrotomatidine                                           0.07006019
## Tomatidine                                                  0.05244799
## Dehydrotomatine 1                                           0.27022641
## Dehydrotomatidine 2                                         0.99828107
## Total Dehydrotomatidine                                     0.42432200
## Alpha-Tomatine                                              0.10179991
## Hydroxytomatidine 1                                        -0.01339938
## Hydroxytomatidine 2                                         0.81594708
## Hydroxytomatidine 3                                         0.11326732
## Hydroxytomatidine 4                                         0.75298938
## Total Hydroxytomatidine                                     0.47729828
## Acetoxytomatidine 1                                         0.98109579
## Acetoxytomatidine 2                                         0.99988232
## Acetoxytomatidine 3                                         0.92522079
## Total Acetoxytomatidine                                     1.00000000
## Dehydrolycoperoside F/G/\nDehydroesculeoside A             -0.10378930
## Lycoperoside F/G/\nEsculeoside A 1                         -0.10520353
## Lycoperoside F/G/\nEsculeoside A 2                         -0.12427680
## Total Lycoperoside F/G/\nEsculeoside A                     -0.12285842
## Escueloside B 1                                            -0.15426327
## Escueloside B 2                                            -0.13869873
## Escueloside B 3                                            -0.12655539
## Total Escueloside B                                        -0.13880876
## Total SteroidalAlkaloids                                    0.75875692
##                                                Dehydrolycoperoside F/G/\nDehydroesculeoside A
## Dehydrotomatidine                                                                  0.06601296
## Tomatidine                                                                         0.03201708
## Dehydrotomatine 1                                                                 -0.08664335
## Dehydrotomatidine 2                                                               -0.11359627
## Total Dehydrotomatidine                                                           -0.10072509
## Alpha-Tomatine                                                                    -0.13481845
## Hydroxytomatidine 1                                                                0.07034779
## Hydroxytomatidine 2                                                                0.27519553
## Hydroxytomatidine 3                                                               -0.06973100
## Hydroxytomatidine 4                                                                0.01655199
## Total Hydroxytomatidine                                                            0.04458027
## Acetoxytomatidine 1                                                                0.01611892
## Acetoxytomatidine 2                                                               -0.10974450
## Acetoxytomatidine 3                                                               -0.07742075
## Total Acetoxytomatidine                                                           -0.10378930
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                                     1.00000000
## Lycoperoside F/G/\nEsculeoside A 1                                                 0.89846235
## Lycoperoside F/G/\nEsculeoside A 2                                                 0.94074883
## Total Lycoperoside F/G/\nEsculeoside A                                             0.93897211
## Escueloside B 1                                                                    0.74998375
## Escueloside B 2                                                                    0.64924390
## Escueloside B 3                                                                    0.82891405
## Total Escueloside B                                                                0.79433475
## Total SteroidalAlkaloids                                                           0.40242306
##                                                Lycoperoside F/G/\nEsculeoside A 1
## Dehydrotomatidine                                                     0.144428573
## Tomatidine                                                            0.057205694
## Dehydrotomatine 1                                                    -0.118537156
## Dehydrotomatidine 2                                                  -0.113985706
## Total Dehydrotomatidine                                              -0.130715137
## Alpha-Tomatine                                                       -0.149345519
## Hydroxytomatidine 1                                                   0.081672380
## Hydroxytomatidine 2                                                   0.276798803
## Hydroxytomatidine 3                                                  -0.075319695
## Hydroxytomatidine 4                                                  -0.002960218
## Total Hydroxytomatidine                                               0.037860385
## Acetoxytomatidine 1                                                  -0.006683375
## Acetoxytomatidine 2                                                  -0.109983729
## Acetoxytomatidine 3                                                  -0.084750923
## Total Acetoxytomatidine                                              -0.105203527
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                        0.898462346
## Lycoperoside F/G/\nEsculeoside A 1                                    1.000000000
## Lycoperoside F/G/\nEsculeoside A 2                                    0.974346889
## Total Lycoperoside F/G/\nEsculeoside A                                0.978601873
## Escueloside B 1                                                       0.893398451
## Escueloside B 2                                                       0.829944521
## Escueloside B 3                                                       0.952867917
## Total Escueloside B                                                   0.931048129
## Total SteroidalAlkaloids                                              0.417979153
##                                                Lycoperoside F/G/\nEsculeoside A 2
## Dehydrotomatidine                                                     0.123633090
## Tomatidine                                                            0.040349303
## Dehydrotomatine 1                                                    -0.121715880
## Dehydrotomatidine 2                                                  -0.134224167
## Total Dehydrotomatidine                                              -0.137159961
## Alpha-Tomatine                                                       -0.160065674
## Hydroxytomatidine 1                                                   0.072080463
## Hydroxytomatidine 2                                                   0.270191759
## Hydroxytomatidine 3                                                  -0.086490154
## Hydroxytomatidine 4                                                  -0.011750855
## Total Hydroxytomatidine                                               0.026460923
## Acetoxytomatidine 1                                                  -0.008802988
## Acetoxytomatidine 2                                                  -0.129892834
## Acetoxytomatidine 3                                                  -0.099822065
## Total Acetoxytomatidine                                              -0.124276805
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                        0.940748826
## Lycoperoside F/G/\nEsculeoside A 1                                    0.974346889
## Lycoperoside F/G/\nEsculeoside A 2                                    1.000000000
## Total Lycoperoside F/G/\nEsculeoside A                                0.999804924
## Escueloside B 1                                                       0.857358670
## Escueloside B 2                                                       0.771054522
## Escueloside B 3                                                       0.915786872
## Total Escueloside B                                                   0.892255795
## Total SteroidalAlkaloids                                              0.408827247
##                                                Total Lycoperoside F/G/\nEsculeoside A
## Dehydrotomatidine                                                         0.125712403
## Tomatidine                                                                0.041911649
## Dehydrotomatine 1                                                        -0.121687192
## Dehydrotomatidine 2                                                      -0.132723980
## Total Dehydrotomatidine                                                  -0.136876387
## Alpha-Tomatine                                                           -0.159453984
## Hydroxytomatidine 1                                                       0.073070504
## Hydroxytomatidine 2                                                       0.271327220
## Hydroxytomatidine 3                                                      -0.085687648
## Hydroxytomatidine 4                                                      -0.011003522
## Total Hydroxytomatidine                                                   0.027515791
## Acetoxytomatidine 1                                                      -0.008635066
## Acetoxytomatidine 2                                                      -0.128412645
## Acetoxytomatidine 3                                                      -0.098704637
## Total Acetoxytomatidine                                                  -0.122858422
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                            0.938972108
## Lycoperoside F/G/\nEsculeoside A 1                                        0.978601873
## Lycoperoside F/G/\nEsculeoside A 2                                        0.999804924
## Total Lycoperoside F/G/\nEsculeoside A                                    1.000000000
## Escueloside B 1                                                           0.862284657
## Escueloside B 2                                                           0.777808453
## Escueloside B 3                                                           0.920924392
## Total Escueloside B                                                       0.897495116
## Total SteroidalAlkaloids                                                  0.410471134
##                                                Escueloside B 1 Escueloside B 2
## Dehydrotomatidine                                   0.23281563      0.21195717
## Tomatidine                                          0.03232704      0.04109465
## Dehydrotomatine 1                                  -0.16856173     -0.16689724
## Dehydrotomatidine 2                                -0.16073421     -0.14376312
## Total Dehydrotomatidine                            -0.18564715     -0.18118200
## Alpha-Tomatine                                     -0.17342845     -0.16358503
## Hydroxytomatidine 1                                 0.05533387      0.05690841
## Hydroxytomatidine 2                                 0.18858965      0.17325154
## Hydroxytomatidine 3                                -0.09182562     -0.08313422
## Hydroxytomatidine 4                                -0.06883813     -0.07136972
## Total Hydroxytomatidine                            -0.01430591     -0.01383502
## Acetoxytomatidine 1                                -0.08533592     -0.08566552
## Acetoxytomatidine 2                                -0.15748758     -0.14109664
## Acetoxytomatidine 3                                -0.13512963     -0.12431715
## Total Acetoxytomatidine                            -0.15426327     -0.13869873
## Dehydrolycoperoside F/G/\nDehydroesculeoside A      0.74998375      0.64924390
## Lycoperoside F/G/\nEsculeoside A 1                  0.89339845      0.82994452
## Lycoperoside F/G/\nEsculeoside A 2                  0.85735867      0.77105452
## Total Lycoperoside F/G/\nEsculeoside A              0.86228466      0.77780845
## Escueloside B 1                                     1.00000000      0.97520927
## Escueloside B 2                                     0.97520927      1.00000000
## Escueloside B 3                                     0.96944469      0.93435394
## Total Escueloside B                                 0.99057297      0.96359429
## Total SteroidalAlkaloids                            0.30551468      0.27483094
##                                                Escueloside B 3
## Dehydrotomatidine                                   0.16931799
## Tomatidine                                          0.05456313
## Dehydrotomatine 1                                  -0.14304066
## Dehydrotomatidine 2                                -0.13340536
## Total Dehydrotomatidine                            -0.15702722
## Alpha-Tomatine                                     -0.15670526
## Hydroxytomatidine 1                                 0.07267780
## Hydroxytomatidine 2                                 0.23476450
## Hydroxytomatidine 3                                -0.08070351
## Hydroxytomatidine 4                                -0.03336692
## Total Hydroxytomatidine                             0.01488154
## Acetoxytomatidine 1                                -0.04979126
## Acetoxytomatidine 2                                -0.13026519
## Acetoxytomatidine 3                                -0.10646100
## Total Acetoxytomatidine                            -0.12655539
## Dehydrolycoperoside F/G/\nDehydroesculeoside A      0.82891405
## Lycoperoside F/G/\nEsculeoside A 1                  0.95286792
## Lycoperoside F/G/\nEsculeoside A 2                  0.91578687
## Total Lycoperoside F/G/\nEsculeoside A              0.92092439
## Escueloside B 1                                     0.96944469
## Escueloside B 2                                     0.93435394
## Escueloside B 3                                     1.00000000
## Total Escueloside B                                 0.99364689
## Total SteroidalAlkaloids                            0.36753214
##                                                Total Escueloside B
## Dehydrotomatidine                                      0.197372597
## Tomatidine                                             0.045981680
## Dehydrotomatine 1                                     -0.155589315
## Dehydrotomatidine 2                                   -0.145448253
## Total Dehydrotomatidine                               -0.170860976
## Alpha-Tomatine                                        -0.164860723
## Hydroxytomatidine 1                                    0.065867082
## Hydroxytomatidine 2                                    0.215499402
## Hydroxytomatidine 3                                   -0.085725303
## Hydroxytomatidine 4                                   -0.049552236
## Total Hydroxytomatidine                                0.002184304
## Acetoxytomatidine 1                                   -0.065997815
## Acetoxytomatidine 2                                   -0.142275790
## Acetoxytomatidine 3                                   -0.119314514
## Total Acetoxytomatidine                               -0.138808756
## Dehydrolycoperoside F/G/\nDehydroesculeoside A         0.794334751
## Lycoperoside F/G/\nEsculeoside A 1                     0.931048129
## Lycoperoside F/G/\nEsculeoside A 2                     0.892255795
## Total Lycoperoside F/G/\nEsculeoside A                 0.897495116
## Escueloside B 1                                        0.990572972
## Escueloside B 2                                        0.963594287
## Escueloside B 3                                        0.993646894
## Total Escueloside B                                    1.000000000
## Total SteroidalAlkaloids                               0.341378371
##                                                Total SteroidalAlkaloids
## Dehydrotomatidine                                             0.1778680
## Tomatidine                                                    0.2898556
## Dehydrotomatine 1                                             0.5112946
## Dehydrotomatidine 2                                           0.7528223
## Total Dehydrotomatidine                                       0.6085030
## Alpha-Tomatine                                                0.3740744
## Hydroxytomatidine 1                                           0.1778832
## Hydroxytomatidine 2                                           0.8699286
## Hydroxytomatidine 3                                           0.2204869
## Hydroxytomatidine 4                                           0.7380949
## Total Hydroxytomatidine                                       0.5670118
## Acetoxytomatidine 1                                           0.8103828
## Acetoxytomatidine 2                                           0.7532965
## Acetoxytomatidine 3                                           0.7712523
## Total Acetoxytomatidine                                       0.7587569
## Dehydrolycoperoside F/G/\nDehydroesculeoside A                0.4024231
## Lycoperoside F/G/\nEsculeoside A 1                            0.4179792
## Lycoperoside F/G/\nEsculeoside A 2                            0.4088272
## Total Lycoperoside F/G/\nEsculeoside A                        0.4104711
## Escueloside B 1                                               0.3055147
## Escueloside B 2                                               0.2748309
## Escueloside B 3                                               0.3675321
## Total Escueloside B                                           0.3413784
## Total SteroidalAlkaloids                                      1.0000000
---
title: "Biosynthesis of Steroidal Alkaloids Are Coordinately Regulated and Differ among Tomatoes in the Red-Fruited Clade, analysis code"
author: "Michael Dzakovich, David Francis, Jessica Cooperstone"
date: "A lot of times"
output:  
  html_document:  
    toc: true  
    toc_float: true
    toc_depth: 3
    theme: lumen
    code_download: true
---

# Introduction

Biosynthesis of Steroidal Alkaloids Are Coordinately Regulated and Differ among Tomatoes in the Red-Fruited Clade

Michael P. Dzakovich1, David M. Francis2, and Jessica L. Cooperstone1,3\*

1 Department of Horticulture and Crop Science, The Ohio State University, 2001 Fyffe Court, Columbus, OH 43210

2 Department of Horticulture and Crop Science, The Ohio State University/Ohio Agricultural Research and Development Center, 1680 Madison Ave., Wooster, OH 44691

3 Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Court., Columbus, OH 43210 \*Corresponding author ([cooperstone.1\@osu.edu](mailto:cooperstone.1@osu.edu){.email})

You can find all the code used to conduct the analyses and create visualizations found in the above paper (and also a number that are not in the paper but relevant). We hope this is helpful!

This work is publicly available as a pre-print on bioRxiv <https://www.biorxiv.org/content/10.1101/2021.01.06.425594v1>, and currently undergoing peer review.

# Load libraries

```{r, message = FALSE}
library(tidyverse) # for wrangling and ggplotting
library(ggrepel) # for repelling text labels
library(readxl) # for reading in excel files
library(scales) # for adjusting comma formatting in axes
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(patchwork) # for multi-panel plot arranging
library(ggdendro) # for making nicer looking dendrograms for hierarchical clustering
library(here) # for directory management
library(knitr) # for knitting and kable()
library(plotly) # for interactive plots
library(corrplot) # for correlation plots
library(ggcorrplot) # for gg correlation plots
```

# Boxplots

Creating one, faceted boxplot to show concentrations of all ten alkaloids (dehydromtomatidine, tomatidine, dehydrotomatine, alpha-tomatine, hydroxytomatine, acetoxytomatine, dehydrolycoperosid/F/G/dehydrescueloside A, lycoperoside F/G/escueloside A, escueloside B and total alkaloids) separated by tomato class (cultivated processing, cultivated cherry, wide-cross hybrid, wild cherry, S. pimpinellifolium).

### Read in data

```{r}
Alkaloids_raw <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "Raw Data Diversity Panel")

# what are the dimensions of this data?
dim(Alkaloids_raw)

# what is the structure of this dataframe?
str(Alkaloids_raw)
```

# Wrangling

## Setting Class as factor

Changing levels in Class so that plots go, left to right: Cultivated Processing, Cultivated Cherry, Wide Cross Hybrid, Wild Cherry, S. pimpinellifolium (from most cultivated, to most wild).

```{r}
Alkaloids_raw$Class <- factor(Alkaloids_raw$Class, 
                              levels = c("Cultivated Processing", 
                                         "Cultivated Cherry", 
                                         "Wide Cross Hybrid",
                                         "Wild Cherry",
                                         "S. pimpinellifolium"))
```

I want to make one boxplot, with alkaloid concentration on the y, and tomato class on the x, that is faceted by alkaloid (i.e., one plot per alkaloid).

## Create tidy data

To do this, we need to data wrangle and convert from wide to long (tidying).

```{r}
# check structure
str(Alkaloids_raw)

# select only the columns we want
Alkaloids_raw_trim <- Alkaloids_raw %>%
  select(Genotype, Class, 
         Dehydrotomatidine, Tomatidine, TotalDehydrotomatine, Tomatine, 
         TotalHydroxytomatine, TotalAcetoxytomatine, DehydrolycoperosideFGdehydroesculeosideA, 
         TotalLycoperosideFGEsculeosideA, TotalEsculeosideB, Total)

# go from wide to long
Alkaloids_tidy <- Alkaloids_raw_trim %>%
  pivot_longer(cols = Dehydrotomatidine:Total,
                names_to = "Alkaloid",
                values_to = "Concentration")

# check structure again  
str(Alkaloids_tidy)
```

## Re-level

Re-level `Alkaloid` so that it is a factor, and goes in pathway order.

```{r}
unique(Alkaloids_tidy$Alkaloid)

# change alkaloid factors to be in biosynthetic pathway order
Alkaloids_tidy$Alkaloid <- factor(Alkaloids_tidy$Alkaloid,
                                  levels = c("Dehydrotomatidine",
                                  "Tomatidine",
                                  "TotalDehydrotomatine",
                                  "Tomatine",
                                  "TotalHydroxytomatine",
                                  "TotalAcetoxytomatine",
                                  "DehydrolycoperosideFGdehydroesculeosideA",
                                  "TotalLycoperosideFGEsculeosideA",
                                  "TotalEsculeosideB",
                                  "Total"))

# check levels
levels(Alkaloids_tidy$Alkaloid)
```

## Set alkaloid and class labels

```{r}
# make a list that has all the full names of the alkaloids
alkaloid_labels <- c("Dehydrotomatidine",
                     "Tomatidine",
                     "Dehydrotomatine",
                     "Alpha-Tomatine",
                     "Hydroxytomatine",
                     "Acetoxytomatine",
                     "Dehydrolycoperoside F/G/Dehydroesculeoside A",
                     "Lycoperoside F/G/Esculeoside A",
                     "Esculeoside B",
                     "Total Steroidal Alkaloids")

# tell alkaloid_labels which original label to refer to
names(alkaloid_labels) <- c("Dehydrotomatidine",
                            "Tomatidine",
                            "TotalDehydrotomatine",
                            "Tomatine",
                            "TotalHydroxytomatine",
                            "TotalAcetoxytomatine",
                            "DehydrolycoperosideFGdehydroesculeosideA",
                            "TotalLycoperosideFGEsculeosideA",
                            "TotalEsculeosideB",
                            "Total")

# enabling labeller for Class too
str(Alkaloids_tidy$Class)
class_labels <- c("Cultivated Processing", 
                  "Cultivated Cherry",
                  "Wide Cross Hybrid", 
                  "Wild Cherry",
                  expression(italic("S. pimpinellifolium")))

class_labels
```

## Plot

## All same y-axis

All y-axis on the same range.

```{r fig.width = 6, fig.height = 9}
Alkaloids_tidy_mg <- Alkaloids_tidy %>%
  mutate(Concentration_mg = Concentration/1000)

Alkaloids_Faceted_Boxplot <- Alkaloids_tidy_mg %>%
  ggplot(aes(x = Class, y = Concentration_mg, fill = Class)) +
  geom_boxplot(aes(x = Class, y = Concentration_mg), alpha = 0.8, outlier.shape = NA) +
  geom_jitter(aes(x = Class, y = Concentration_mg, fill = Class), 
              shape = 21, size = 0.5, alpha = 0.6) +
  scale_y_continuous(trans = "log10", labels = scales::number_format()) + 
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  labs(title = "Steroidal Alkaloid Concentrations in Tomato",
       y = "mg/100 g fresh weight") +
  theme_classic() +
  theme(legend.text.align = 0,
        legend.position = "top",
        legend.title = element_blank(),
        title = element_text(size = 14),
        axis.title = element_text(size = 10),
        strip.text = element_text(size = 8),
        legend.text = element_text(size = 6),
        axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_text(size = 6),
        panel.grid.major.y = element_line(colour = "gray50", size = 0.1, linetype = "dashed")) +
  facet_wrap(~Alkaloid, ncol = 2, nrow = 5,
             labeller = labeller(Alkaloid = alkaloid_labels)) +
  annotation_logticks(sides = "l", size = 0.15) 

Alkaloids_Faceted_Boxplot
```

```{r, eval = FALSE}
ggsave("Alkaloid_facet_mg_smallerdots_thinnerticks.pdf", 
       plot = Alkaloids_Faceted_Boxplot,
       dpi = 800,
       width = 6, height = 9)
```

## Label both columns of y-axis

All y-axis on the same range, but the y-axis in the right column is now labelled.

```{r, fig.width = 6, fig.height = 9}
Alkaloids_Faceted_Boxplot_bothyaxislabelled <- Alkaloids_tidy_mg %>%
  ggplot(aes(x = Class, y = Concentration_mg, fill = Class)) +
  geom_boxplot(aes(x = Class, y = Concentration_mg), alpha = 0.8, outlier.shape = NA) +
  geom_jitter(aes(x = Class, y = Concentration_mg, fill = Class), 
              shape = 21, size = 0.5, alpha = 0.6) +
  scale_y_continuous(trans = "log10", labels = scales::number_format(),
                     limits = c(0.00001, 100)) + 
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  labs(title = "Steroidal Alkaloid Concentrations in Tomato",
       y = "mg/100 g fresh weight") +
  theme_classic() +
  theme(legend.text.align = 0,
        legend.position = "top",
        legend.title = element_blank(),
        title = element_text(size = 14),
        axis.title = element_text(size = 10),
        strip.text = element_text(size = 8),
        legend.text = element_text(size = 6),
        axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_text(size = 6),
        panel.grid.major.y = element_line(colour = "gray50", size = 0.1, linetype = "dashed")) +
  facet_wrap(~Alkaloid, ncol = 2, nrow = 5,
             labeller = labeller(Alkaloid = alkaloid_labels),
             scales = "free_y") +
  annotation_logticks(sides = "l", size = 0.15) 

Alkaloids_Faceted_Boxplot_bothyaxislabelled
```

```{r, eval = FALSE}
ggsave("Alkaloid_facet_mg_smallerdots_thinnerticks_all_y_labelled.pdf", 
       plot = Alkaloids_Faceted_Boxplot_bothyaxislabelled,
       dpi = 800,
       width = 6, height = 9)
```

## Free y-axis per alkaloid

Free\_y axis (meaning all the y-axis are independent). This is the plot we used in the paper.

```{r, fig.width = 6, fig.height = 9}
Alkaloids_Faceted_Boxplot_free_y <- Alkaloids_tidy_mg %>%
  ggplot(aes(x = Class, y = Concentration_mg, fill = Class)) +
  geom_boxplot(aes(x = Class, y = Concentration_mg), alpha = 0.8, outlier.shape = NA) +
  geom_jitter(aes(x = Class, y = Concentration_mg, fill = Class), 
              shape = 21, size = 0.5, alpha = 0.6) +
  scale_y_continuous(trans = "log10", labels = scales::number_format()) + 
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  labs(title = "Steroidal Alkaloid Concentrations in Tomato",
       y = "mg/100 g fresh weight") +
  theme_classic() +
  theme(legend.text.align = 0,
        legend.position = "top",
        legend.title = element_blank(),
        title = element_text(size = 14),
        axis.title = element_text(size = 10),
        strip.text = element_text(size = 8),
        legend.text = element_text(size = 6),
        axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_text(size = 6),
        panel.grid.major.y = element_line(colour = "gray50", size = 0.1, linetype = "dashed")) +
  facet_wrap(~Alkaloid, ncol = 2, nrow = 5,
             labeller = labeller(Alkaloid = alkaloid_labels),
             scales = "free_y") +
  annotation_logticks(sides = "l", size = 0.15) 

Alkaloids_Faceted_Boxplot_free_y
```

```{r, eval = FALSE}
ggsave("Alkaloid_facet_mg_smallerdots_thinnerticks_free_y.pdf", 
       plot = Alkaloids_Faceted_Boxplot_free_y,
       dpi = 800,
       width = 6, height = 9)
```

# PCA

### Read in data

```{r}
Alkaloids_LSmeans <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "LSMeans Diversity Panel")

str(Alkaloids_LSmeans)
```

## Prep data for PCA

Select only the data we will use for our PCA. This includes the alkaloid totals (vs each individual isomer, which is listed), and removing some of the meta-data that we don't need to complicate our lives right now.

```{r}
Alkaloids_LSmeans_forPCA <- Alkaloids_LSmeans %>%
  select(Genotype, Class,
         Dehydrotomatidine, Tomatidine,
         TotalDehydrotomatine, Tomatine,
         TotalHydroxytomatine, TotalAcetoxytomatine,
         DehydrolycoperosideFGdehydroesculeosideA, TotalLycoperosideFGEsculeosideA,
         TotalEsculeosideB, Total)
```

## Run PCA

We are running PCA using the package [`FactoMineR`](https://cran.r-project.org/web/packages/FactoMineR/index.html), and doing a quick visualization with `fviz_pca` from `FactoExtra`.

`quali.sup` gives the indexes of the qualitative/categorical variables, in this case, `Genotype` and `Class`, which aren't inputs to the PCA. `scale.unit` is set to `TRUE` because we want to scale data to unit variance. `scale.unit = TRUE` is the default.

```{r}
# run PCA
Alkaloids_PCA <- PCA(Alkaloids_LSmeans_forPCA, 
                     quali.sup = 1:2, 
                     graph = FALSE,
                     scale.unit = TRUE)

# visualize the PCA and loadings plot really quickly
fviz_pca(Alkaloids_PCA)

# visualize the PCA scores plot quickly
fviz_pca_ind(Alkaloids_PCA)
```

This PCA scores plot isn't too useful for us because we don't know what point is what (they are just ordered as in our df) but we can fix this in a minute.

```{r}
# visualize the PCA loadings plot quickly
fviz_pca_var(Alkaloids_PCA)

# get a scree plot
fviz_eig(Alkaloids_PCA)
```

## Get scores and loadings coordinates

These default plots would benefit from plotting with `ggplot2` so that:

-   we can see how things like `Class` affect scores plot locations
-   so we can make these plots prettier

Get coordinates for scores plot (where each point is an individual, i.e., a tomato accession). Then bind back to relevant meta-data.

```{r}
# get scores coordinates
scores_coordinates <- as.data.frame(Alkaloids_PCA$ind$coord)

# check structure
str(scores_coordinates)

# bind back to meta-data
Alkaloids_LSmeans_withPC <- bind_cols(Alkaloids_LSmeans, scores_coordinates)
```

Now the first 5 dimensions (Dim.1 through Dim.5) are at the end of our df.

Get coordinates for loadings plot (where each point is a variable, i.e., a alkaloid). Then bind back to indicate which alkaloid is which set of coordinates.

```{r}
loadings_coordinates <- as.data.frame(Alkaloids_PCA$var$coord)

str(loadings_coordinates)

loadings_coordinates$Alkaloid <- c("Dehydrotomatidine",
                                  "Tomatidine",
                                  "TotalDehydrotomatine",
                                  "Tomatine",
                                  "TotalHydroxytomatine",
                                  "TotalAcetoxytomatine",
                                  "DehydrolycoperosideFGdehydroesculeosideA",
                                  "TotalLycoperosideFGEsculeosideA",
                                  "TotalEsculeosideB",
                                  "Total")

str(loadings_coordinates)
```

Set `Class` as a factor with levels going from more cultivated, to more wild, in the order specified below.

```{r}

Alkaloids_LSmeans_withPC$Class <- factor(Alkaloids_LSmeans_withPC$Class, 
                                         levels = c("Cultivated Processing", 
                                                    "Cultivated Cherry", 
                                                    "Wide Cross Hybrid",
                                                    "Wild Cherry",
                                                    "S. pimpinellifolium"))
```

## Plot

### PCA Scores Plot

```{r}
Alkaloids_PCA_scores <- Alkaloids_LSmeans_withPC %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_point(size = 2.5, color = "black", alpha = 0.8) +
  scale_shape_manual(values= c(24, 22, 23, 22, 21),
                     labels = class_labels) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = class_labels) +
  xlim(-2, 6) +
  ylim(-5, 6) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(x = "PC1: 32.2%",
       y = "PC2: 30.0%")

Alkaloids_PCA_scores
```

```{r, eval = FALSE}
ggsave(filename = "PCA_scores.png",
       plot = Alkaloids_PCA_scores,
       dpi = 1200,
       width = 9,
       height = 6)
```

### PCA Loadings Plot

```{r}
Alkaloids_PCA_loadings <- loadings_coordinates %>%
  ggplot(aes(x = Dim.1, y = Dim.2, label = Alkaloid)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_point(size = 2.5, color = "black", alpha = 0.8) +
  geom_text_repel(aes(label = alkaloid_labels)) +
  xlim(-0.33,1.1) +
  ylim(-0.8, 1) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(x = "PC1: 32.2%",
       y = "PC2: 30.0%")

Alkaloids_PCA_loadings
```

```{r, eval = FALSE}
ggsave(filename = "PCA_loadings.png",
       plot = Alkaloids_PCA_loadings,
       dpi = 1200,
       width = 9,
       height = 6)
```

Make one plot of the PCA scores and loadings with the package `patchwork`

```{r}
scores_and_loadings <- Alkaloids_PCA_scores + Alkaloids_PCA_loadings

scores_and_loadings + plot_annotation(tag_levels = "A")

scores_and_loadings <- scores_and_loadings + plot_annotation(tag_levels = "A")
```

```{r, eval = FALSE}
ggsave(filename = "PCA_scores_and_loadings.png",
       plot = scores_and_loadings,
       dpi = 1200,
       width = 14,
       height = 6)
```

# Hierarchical clustering

Data is already read in from the PCA section

```{r}
glimpse(Alkaloids_LSmeans)

# we can use this df for HCA also with some trimming
glimpse(Alkaloids_LSmeans_forPCA)
```

We will move `Genotype` to a rowname, because in Hierarchical Clustering you can only have your input data (i.e., no meta-data). Having `Genotype` as a rowname helps recombination with metadata later.

```{r}
Alkaloids_LSmeans_forHCA <- Alkaloids_LSmeans_forPCA %>%
  column_to_rownames(var = "Genotype") %>%
  select(-Class)

kable(head(Alkaloids_LSmeans_forHCA))
```

#### Do we need to scale?

```{r}
colMeans(Alkaloids_LSmeans_forHCA) # calculate means

# 2 means by column-wise
apply(Alkaloids_LSmeans_forHCA, 2, sd) # calculate SDs
```

Yes we do need to scale because we do not have means of 0 and SD of 1.

### Scaling

```{r}
scaled_Alkaloids_LSmeans_forHCA <- scale(Alkaloids_LSmeans_forHCA)
```

Did the scaling work?

```{r}
colMeans(scaled_Alkaloids_LSmeans_forHCA) # calculate means, should be 0

apply(scaled_Alkaloids_LSmeans_forHCA, 2, sd) # calculate SDs, should be 1
```

Yes scaling worked. Now we can continue.

### Generate distance matrix

```{r}
# calculate the distance matrix (euclidean distance)
dist_matrix <- dist(scaled_Alkaloids_LSmeans_forHCA) 

# conduct hierarchical clustering
alkaloid_hclust <- hclust(d = dist_matrix, method = "ward.D2")

summary(alkaloid_hclust)
```

### Plot dendrogram of `alkaloid_hclust`

Make a quick and dirty plot of our hierarchical clustering, will make look nicer later.

```{r, fig.height = 6, fig.width = 8}
hclust_ward2 <- plot(alkaloid_hclust, 
                    main = "HCA of Accessions by Steroidal Alkaloid Profile")
```

## Making dendrogram look nice

You apply `dendro_data()` on a HCA object, here `alkaloid_hclust`

```{r, fig.width=7, fig.height=4}
# extract data for dendrogram.  can use rectangle or triangle
dend_data <- dendro_data(alkaloid_hclust, type = "rectangle")

# what are the names in dend_data
names(dend_data)

# ggplot dendrogram from hclust
ggdendrogram(alkaloid_hclust)
```

#### Wrangling end labels

```{r}
# line segments
head(dend_data$segments) 

# labels
head(dend_data$labels)

# make vector with the end label order
end_label_order <- dend_data$labels$label

# make df with end_label_order and an id for order
# rename end_label_order to be Genotype to enable joining
end_label_order_withid <- as.data.frame(end_label_order) %>%
  mutate(hclust_end_order = row_number()) %>%
  rename(Genotype = end_label_order)

# convert rowname to column in scaled HCA data to enable joining
scaled_Alkaloids_LSmeans_forHCA_genotype <- 
  as.data.frame(scaled_Alkaloids_LSmeans_forHCA) %>%
  rownames_to_column(var = "Genotype")

class(scaled_Alkaloids_LSmeans_forHCA_genotype)
class(end_label_order_withid)

# join with meta-data by 
Alkaloids_LSmeans_forHCA_endlabelorder_fordend <- 
  left_join(Alkaloids_LSmeans_withPC, 
            end_label_order_withid, 
            by = "Genotype")

kable(head(Alkaloids_LSmeans_forHCA_endlabelorder_fordend))
```

```{r, fig.width = 8}
# arrange by end label order
order_for_Genotype_endlabels <- 
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend %>%
  arrange(hclust_end_order)

order_for_Genotype_endlabels$Class <- factor(order_for_Genotype_endlabels$Class, 
                                                 levels = c("Cultivated Processing", 
                                                            "Cultivated Cherry", 
                                                            "Wide Cross Hybrid", 
                                                            "Wild Cherry", 
                                                            "S. pimpinellifolium"))

HCA_tomato_alkaloids_wardD2 <- dend_data$segments %>%
  ggplot(aes(x = x, y = y)) +
  geom_segment(aes(xend = xend, yend = yend)) +
  geom_text(data = dend_data$labels, 
            aes(x, y, label = order_for_Genotype_endlabels$Genotype, 
                color = order_for_Genotype_endlabels$Class), 
            hjust = 1, angle = 90, size = 2.5) +
  scale_color_manual(values = c("purple", "blue", "limegreen", "gold", "red2")) +
  ylim(-5, 25) +
  theme_minimal() +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank()) +
  labs(title = "Dendrogram from Hierarhical Clustering of Tomato Steroidal Alkaloids",
       subtitle = "Ward's D2 Linkage Distances",
       x = "",
       y = "Height",
       color = "Class")

HCA_tomato_alkaloids_wardD2
```

## Cut tree at k = 3

Selected based on major clusters of the dendrogram above.

```{r}
cut3.alkaloid_hclust <- cutree(alkaloid_hclust, k = 3)
hclust_3_group <- cut3.alkaloid_hclust

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend %>%
  mutate(hclust_3_cluster = as.factor(hclust_3_group))

glimpse(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust)
```

# Superimpose clusters onto PCA

## Regular PCA (no cluster assignment)

We have made this plot already in the PCA section of this Rmd.

```{r}
Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust$Class <- 
  factor(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust$Class, 
         levels = c("Cultivated Processing", 
                    "Cultivated Cherry", 
                    "Wide Cross Hybrid", 
                    "Wild Cherry", 
                    "S. pimpinellifolium"))

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class)) +
  geom_point(size = 2, alpha = 0.8) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  scale_shape_manual(values=c(24,22,23,22,21),
                     labels = expression("Cultivated Processing", 
                                         "Cultivated Cherry", 
                                         "Wide Cross Hybrid", 
                                         "Wild Cherry", italic("S. pimpinellifolium"))) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2"),
                    labels = expression("Cultivated Processing", 
                                        "Cultivated Cherry", 
                                        "Wide Cross Hybrid", 
                                        "Wild Cherry", 
                                        italic("S. pimpinellifolium"))) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(title = "PCA Scores Plot",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") 

```

Labeling points with their genotype.

```{r}
alkaloid_pca <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class, label = Genotype)) +
  geom_point() +
  geom_text(nudge_x = 0.8, nudge_y = 0.1) +
  scale_shape_manual(values=c(24,22,23,22,21)) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2")) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12)) +
  labs(title = "",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%")

alkaloid_pca
```

A disaster. Using `ggrepel` doesn't work super well here either because there is so much bunching of points.

Let's try using plotly.

```{r}
alkaloid_pca <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, 
             fill = Class, shape = Class, label = Genotype, text = Genotype)) +
  geom_point(size = 2, alpha = 0.8) +
  scale_shape_manual(values=c(24,22,23,22,21)) +
  scale_fill_manual(values = c("purple", "blue", "limegreen", "gold", "red2")) +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12)) +
  labs(title = "PCA Scores Plot",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%")

ggplotly(alkaloid_pca, tooltip = "text")
```

### PCA with HCA Clusters

#### 3 clusters

```{r}
Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = hclust_3_cluster, shape = Class)) +
  geom_point(size = 2, alpha = 0.8) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  scale_shape_manual(values=c(24,22,23,22,21),
                     labels = expression("Cultivated Processing", 
                                         "Cultivated Cherry", 
                                         "Wide Cross Hybrid", 
                                         "Wild Cherry", 
                                         italic("S. pimpinellifolium"))) +
  scale_fill_viridis_d() +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(title = "PCA Scores Plot",
       subtitle = "Overlaid with Hierarchical Cluster Assignment (3 clusters)",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") 
```

## PCA of wild cherry only

Remove all other accessions that do not belong to the class wild cherry.

```{r}
Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry <- 
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust %>%
  filter(Class == "Wild Cherry")

Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  ggplot(aes(x = Dim.1, y = Dim.2, fill = Class, shape = Class)) +
  geom_point(size = 2.5, alpha = 0.8) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black", alpha = 0.6) +
  scale_shape_manual(values=22) +
  scale_fill_manual(values = "gold")  +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.text.align = 0) +
  labs(title = "",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") + 
  theme(legend.position="right") 
```

## HCA overlaid on wild cherry

### 3 clusters

```{r}
glimpse(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry)

(PCA_3_HCA <- Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  ggplot(aes(x = Dim.1, y = Dim.2, 
             fill = hclust_3_cluster, 
             label = hclust_3_cluster)) +
  geom_point(size = 2.5, alpha = 0.8, shape = 22) +
  scale_fill_viridis_d() +
  labs(title = "PCA Scores Plot Colored by Hierarchical Cluster",
       subtitle = "Ward's D2 Linkage, Wild Cherry Only",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.position = "none"))
```

```{r, eval = FALSE}
ggsave(filename = "HCA_3clusters.png",
       plot = PCA_3_HCA,
       dpi = 1200,
       width = 9,
       height = 6)
```

### 3 clusters with accessions labelled

Labelling the accessions in the bottom cluster.

```{r}
kable(head(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry))

# create character vector of Genotypes in bottom cluster
bottom_wild_cherry_forfilt <- c("LA2126A",
                       "LA1338",
                        "LA1654",
                        "LA2262",
                        "LA2213",
                        "LA2256",
                        "LA2308",
                        "LA1701",
                        "PI155372",
                        "PI129128")

# create new df which only includes those bottom cluster Genotypes
Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry_bottom <-
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  filter(Genotype %in% bottom_wild_cherry_forfilt)

dim(Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry_bottom)

# get the same location of labels each time
set.seed(123) # because geom_label_repel iterates

(PCA_3_HCA_bottomclusterlabelled <- 
  Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry %>%
  ggplot(aes(x = Dim.1, y = Dim.2, 
             fill = hclust_3_cluster, 
             label = hclust_3_cluster)) +
  geom_point(size = 2.5, alpha = 0.8, shape = 22) +
  geom_label_repel(data = Alkaloids_LSmeans_forHCA_endlabelorder_fordend_withclust_wildcherry_bottom,
                   aes(label = Genotype), fill = "white") +
  scale_fill_viridis_d() +
  labs(title = "PCA Scores Plot Colored by Hierarchical Cluster",
       subtitle = "Ward's D2 Linkage, Wild Cherry Only",
       x = "PC1: 32.2%",
       y = "PC2: 30.0%") +
  theme_classic() + 
  theme(text = element_text(size = 12),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 12),
        legend.position = "none"))
```

```{r, eval = FALSE}
ggsave(filename = "HCA_3clusters_bottomclusterlabelled.jpg",
       plot = PCA_3_HCA_bottomclusterlabelled,
       dpi = 1200,
       width = 9,
       height = 6)
```

# GWAS

UPLOADING SOON.

# Manhattan Plots

Create Manhattan plots from the GWAS and mQTL analysis in our diversity panel and bi-parental populations, respectively. Selection criteria included known patterns of genetic diversity as determined by genotyping using a 7,720 SNP array ([Sim et al., 2012a](https://doi.org/10.1371/journal.pone.0045520); [Blanca et al., 2015](https://doi.org/10.1186/s12864-015-1444-1)), previous phenotypic information for alkaloid content ([Rick et al., 1994](https://doi.org/10.1073/pnas.91.26.12877)), and geographic origin (visualized in Fig. S1.). Alkaloids were phenotyped using our previously published method ([Dzakovich et al., 2020](https://doi.org/10.3389/fpls.2020.00767)).

## Diversity panel GWAS

### Read in data

```{r}
diversity_GWAS_raw <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "GWAS Output_MAF10Pct")

# what are the dimensions of this data?
dim(diversity_GWAS_raw)

# what is the structure of this dataframe?
str(diversity_GWAS_raw)
```

## Wrangling

Making data tidy facilitates creating Manhattan plots that are faceted by each different alkaloid.

```{r}
diversity_GWAS_tidy <- diversity_GWAS_raw %>%
  pivot_longer(cols = Dehydrotomatidine_neg_log10_pval:Total_neg_log10_pval, 
               names_to = "alkaloid", 
               values_to = "pvalue")

# check dataframe dimensions
dim(diversity_GWAS_tidy)
```

Setting up labels for use with `labeller`.

```{r}
alk_labels <- c(
  Dehydrotomatidine_neg_log10_pval = "Dehydrotomatidine",
  Tomatidine_neg_log10_pval = "Tomatidine",
  Dehydrotomatine_neg_log10_pval = "Dehydrotomatine",
  Alpha_Tomatine_neg_log10_pval = "Alpha-Tomatine",
  Hydroxytomatine_neg_log10_pval = "Hydroxytomatine",
  Acetoxytomatine_neg_log10_pval = "Acetoxytomatine",
  DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval = "Dehydrolycoperoside F/G/Dehydroesculeoside A",
  LycoperosideF_G_Esculeoside_A_neg_log10_pval = "Lycoperoside F/G/Esculeoside A",
  Esculeoside_B_neg_log10_pval = "Esculeoside B",
  Total_neg_log10_pval = "Total Steroidal Alkaloids")
```

## Alkaloid as factors

This will make our faceted plot go in the alkaloid biosynthetic pathway order, instead of alphabetically.

```{r}
diversity_GWAS_tidy$alkaloid <- 
  factor(diversity_GWAS_tidy$alkaloid, 
         levels = c("Dehydrotomatidine_neg_log10_pval", 
                    "Tomatidine_neg_log10_pval", 
                    "Dehydrotomatine_neg_log10_pval", 
                    "Alpha_Tomatine_neg_log10_pval", 
                    "Hydroxytomatine_neg_log10_pval", 
                    "Acetoxytomatine_neg_log10_pval", 
                    "DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval",
                    "LycoperosideF_G_Esculeoside_A_neg_log10_pval",
                    "Esculeoside_B_neg_log10_pval",
                    "Total_neg_log10_pval"))

str(diversity_GWAS_tidy)

class(diversity_GWAS_tidy$alkaloid)
levels(diversity_GWAS_tidy$alkaloid)
```

## Faceting by alkaloid GWAS

Here we have one mondo figure with all the alkaloid Manhattan plots on the same axis. Points are colored red if they are significant at the 0.05 level after a FDR correction. The input data are FDR corrected, and a -log10pvalue of 1.301 is the same as P = 0.05 FDR corrected.

```{r}
diversity_GWAS_tidy %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), y = pvalue, group = alkaloid)) +
  geom_point(aes(color = pvalue > 1.301), size=.5, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = c("black", "red")) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("-log"[10]* " FDR Corrected P-Value")) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28))) 
```

### Alternating chromosome colors

This will make it easier to tell which markers are on which chromosome.

```{r, fig.width = 10, fig.height = 6}
diversity_GWAS_alt_chr <- diversity_GWAS_tidy %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(CHR), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free_x") 

diversity_GWAS_alt_chr
```

```{r, eval = FALSE}
ggsave("Diversity_GWAS_alt_chr.png",
       plot = diversity_GWAS_alt_chr,
       dpi = 1200,
       width = 10,
       height = 6)
```

### Free y-axis

All y-axis scales are independent.

```{r, fig.width = 10, fig.height = 6}
diversity_GWAS_alt_chr_free_y <- diversity_GWAS_tidy %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(CHR), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free") 

diversity_GWAS_alt_chr_free_y
```

```{r, eval = FALSE}
ggsave("Diversity_GWAS_alt_chr_free_y.png",
       plot = diversity_GWAS_alt_chr_free_y,
       dpi = 1200,
       width = 10,
       height = 6)
```

### Individual Manhattan plots for each alkaloid

```{r}
kable(head(diversity_GWAS_raw))

diversity_GWAS_manhattan_plots <- function(g){
diversity_GWAS_raw %>%
  ggplot(aes(x = (CHR+(Phy_Posbp/75000000)), color = as.factor(CHR))) +
  geom_point(aes_(y = as.name(g)), size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(diversity_GWAS_tidy$CHR), 
                     label = unique(diversity_GWAS_tidy$CHR)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  labs(title = paste(g),
       x ="Chromosome",
       y = (expression("FDR corrected "-log[10](P))))
}  

all_diversity_gwas_manhatan_plots <- lapply(names(diversity_GWAS_raw[,6:ncol(diversity_GWAS_raw)]), 
                                            diversity_GWAS_manhattan_plots)
all_diversity_gwas_manhatan_plots
```

## Bi-parental GWAS

### Read in data

```{r}
biparental_GWAS_raw <- read_excel("SteroidalAlkaloidsSupplementalData.xlsx",
                            sheet = "BC1S1 Output MAF10Pct")

# what are the dimensions of this data?
dim(biparental_GWAS_raw)

# what is the structure of this dataframe?
str(biparental_GWAS_raw)
```

## Wrangling

Making data tidy facilitates faceting by alkaloids.

```{r}
biparental_GWAS_tidy <- biparental_GWAS_raw %>%
  pivot_longer(cols = Dehydrotomatidine_neg_log10_pval:Total_neg_log10_pval, 
               names_to = "alkaloid", 
               values_to = "pvalue")

# check dataframe dimensions
dim(biparental_GWAS_tidy)
```

## Alkaloid as factors

This will make our faceted plot go in the alkaloid biosynthetic pathway order, instead of alphabetically.

```{r}
biparental_GWAS_tidy$alkaloid <- 
  factor(biparental_GWAS_tidy$alkaloid, 
         levels = c("Dehydrotomatidine_neg_log10_pval", 
                    "Tomatidine_neg_log10_pval", 
                    "Dehydrotomatine_neg_log10_pval", 
                    "Alpha_Tomatine_neg_log10_pval", 
                    "Hydroxytomatine_neg_log10_pval", 
                    "Acetoxytomatine_neg_log10_pval", 
                    "DehydrolycoperosideF_G_Dehydroesculeoside_A_neg_log10_pval",
                    "LycoperosideF_G_Esculeoside_A_neg_log10_pval",
                    "Esculeoside_B_neg_log10_pval",
                    "Total_neg_log10_pval"))

str(biparental_GWAS_tidy)

class(biparental_GWAS_tidy$alkaloid)
levels(biparental_GWAS_tidy$alkaloid)
```

## Faceting by alkaloid GWAS

Here we have one mondo figure with all the alkaloid Manhattan plots on the same axis. Points are colored red if they are significant at the 0.05 level after a FDR correction. The input data are FDR corrected, and a -log10pvalue of 1.301 is the same as P = 0.05 FDR corrected.

```{r}
biparental_GWAS_tidy %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), y = pvalue, group = alkaloid)) +
  geom_point(aes(color = pvalue > 1.301), size=.5, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_tidy$Chr), 
                     label = unique(biparental_GWAS_tidy$Chr)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = c("black", "red")) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("-log"[10]* " FDR Corrected P-Value")) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28))) 
```

### Alternating chromosome colors

```{r, fig.width = 10, fig.height = 6}
biparental_GWAS_alt_chr <- biparental_GWAS_tidy %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(Chr), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_tidy$Chr), 
                     label = unique(biparental_GWAS_tidy$Chr)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free_x") 

biparental_GWAS_alt_chr
```

```{r, eval = FALSE}
ggsave("biparental_GWAS_alt_chr.png",
       plot = biparental_GWAS_alt_chr,
       dpi = 1200,
       width = 10,
       height = 6)
```

### Free y-axis

```{r, fig.width = 10, fig.height = 6}
biparental_GWAS_alt_chr_free_y <- biparental_GWAS_tidy %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), y = pvalue, 
             color = as.factor(Chr), group = alkaloid)) +
  geom_point(size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_tidy$Chr), 
                     label = unique(biparental_GWAS_tidy$Chr)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  xlab("Chromosome") +
  ylab(expression("FDR corrected "-log[10](P))) +
  facet_wrap(~alkaloid, 
             nrow = 2, 
             ncol = 5,
             labeller = as_labeller(alk_labels, default = label_wrap_gen(28)),
             scales = "free") 

biparental_GWAS_alt_chr_free_y
```

```{r, eval = FALSE}
ggsave("biparental_GWAS_alt_chr_free_y.png",
       plot = biparental_GWAS_alt_chr_free_y,
       dpi = 1200,
       width = 10,
       height = 6)
```

### Individual Manhattan plots for each alkaloid

```{r}
kable(head(biparental_GWAS_raw))

biparental_GWAS_manhattan_plots <- function(g){
biparental_GWAS_raw %>%
  ggplot(aes(x = (Chr+(Phy_Posbp/75000000)), color = as.factor(Chr))) +
  geom_point(aes_(y = as.name(g)), size=1, stat = "identity") +
  scale_x_continuous(breaks = unique(biparental_GWAS_raw$Chr), 
                     label = unique(biparental_GWAS_raw$Chr)) +
  geom_hline(aes(yintercept= 1.301), color = "grey", linetype = "dashed") +
  scale_color_manual(values = rep(c("black", "gray"), 12)) +
  theme_classic() +
  theme(legend.position = "none") +
  labs(title = paste(g),
       x ="Chromosome",
       y = (expression("FDR corrected "-log[10](P))))
}  

all_biparental_gwas_manhatan_plots <- lapply(names(biparental_GWAS_raw[,8:ncol(biparental_GWAS_raw)]), 
                                            biparental_GWAS_manhattan_plots)
all_biparental_gwas_manhatan_plots
```

## Correlation Analysis

We are interested to understand how the concentration of each alkaloid is related to every other alkaloid. To do this, we will conduct a correlation analysis.

The inputs to the correlation analyses are the LS means per genotype.

```{r}
head(Alkaloids_LSmeans)

# this is already trimmed of some meta-data we don't need
head(Alkaloids_LSmeans_forPCA)
```

```{r}
# all isomers
Alkaloids_LSmeans_allisomers_corr <- Alkaloids_LSmeans %>%
  select(Genotype, Class, Dehydrotomatidine:Total)

# sums only
Alkaloids_LSmeans_sums_corr <- Alkaloids_LSmeans_forPCA %>%
  select(Genotype, Class, Dehydrotomatidine:Total)
```

### On sums

#### Full population

```{r}
Alkaloids_sums_corr_matrix <- cor(Alkaloids_LSmeans_sums_corr[,3:ncol(Alkaloids_LSmeans_sums_corr)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix)

# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest <- cor.mtest(Alkaloids_sums_corr_matrix, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix <- sums_cor_mtest$p
```

Prettying up the axis labels

```{r}
axis_labels_totals <- c("Dehydrotomatidine",
                 "Tomatidine",
                 "Dehydrotomatine",
                 "Alpha-Tomatine",
                 "Hydroxytomatidine",
                 "Acetoxytomatidine",
                 "Dehydrolycoperoside F/G/Dehydroesculeoside A",
                 "Lycoperoside F/G/Esculeoside A",
                 "Escueloside B",
                 "Total SteroidalAlkaloids")

colnames(Alkaloids_sums_corr_matrix) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix) <- axis_labels_totals %>% str_wrap(width = 24)
```

Create corrplot

```{r}
corrplot_sum_fullpop <- corrplot(Alkaloids_sums_corr_matrix,
         method = "circle",
         p.mat = sums_pval_matrix,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col="black", # color of axis text
         title = "Full Population") 

corrplot_sum_fullpop
```

#### Cultivated Processing

```{r}
Alkaloids_LSmeans_sums_corr_cultproc <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Cultivated Processing")

head(Alkaloids_LSmeans_sums_corr_cultproc)

Alkaloids_sums_corr_matrix_cultproc <- cor(Alkaloids_LSmeans_sums_corr_cultproc[,3:ncol(Alkaloids_LSmeans_sums_corr_cultproc)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_cultproc)

# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_cultproc <- cor.mtest(Alkaloids_sums_corr_matrix_cultproc, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_cultproc <- sums_cor_mtest_cultproc$p

colnames(Alkaloids_sums_corr_matrix_cultproc) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_cultproc) <- axis_labels_totals %>% str_wrap(width = 24)
```

Create corrplot

```{r}
corrplot_sum_cultproc <- corrplot(Alkaloids_sums_corr_matrix_cultproc,
         method = "circle",
         p.mat = sums_pval_matrix_cultproc,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Cultivated Processing") 

corrplot_sum_cultproc
```

#### Cultivated Cherry

```{r}
Alkaloids_LSmeans_sums_corr_cultcherry <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Cultivated Cherry")

head(Alkaloids_LSmeans_sums_corr_cultcherry)

Alkaloids_sums_corr_matrix_cultcherry <- cor(Alkaloids_LSmeans_sums_corr_cultcherry[,3:ncol(Alkaloids_LSmeans_sums_corr_cultcherry)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_cultcherry)

# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_cultcherry <- cor.mtest(Alkaloids_sums_corr_matrix_cultcherry, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_cultcherry <- sums_cor_mtest_cultcherry$p

colnames(Alkaloids_sums_corr_matrix_cultcherry) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_cultcherry) <- axis_labels_totals %>% str_wrap(width = 24)
```

Create corrplot

```{r}
corrplot_sum_cultcherry <- corrplot(Alkaloids_sums_corr_matrix_cultcherry,
         method = "circle",
         p.mat = sums_pval_matrix_cultcherry,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Cultivated Cherry") 

corrplot_sum_cultcherry
```

#### Wide Cross Hybrid

```{r}
Alkaloids_LSmeans_sums_corr_hybrid <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Wide Cross Hybrid")

head(Alkaloids_LSmeans_sums_corr_hybrid)

Alkaloids_sums_corr_matrix_hybrid <- cor(Alkaloids_LSmeans_sums_corr_hybrid[,3:ncol(Alkaloids_LSmeans_sums_corr_hybrid)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_hybrid)

# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_hybrid <- cor.mtest(Alkaloids_sums_corr_matrix_hybrid, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_hybrid <- sums_cor_mtest_hybrid$p

colnames(Alkaloids_sums_corr_matrix_hybrid) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_hybrid) <- axis_labels_totals %>% str_wrap(width = 24)
```

Create corrplot

```{r}
corrplot_sum_hybrid <- corrplot(Alkaloids_sums_corr_matrix_hybrid,
         method = "circle",
         p.mat = sums_pval_matrix_hybrid,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Wide Cross Hybrid") 

corrplot_sum_hybrid
```

#### Wild Cherry

```{r}
Alkaloids_LSmeans_sums_corr_wildcherry <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "Wild Cherry")

head(Alkaloids_LSmeans_sums_corr_wildcherry)

Alkaloids_sums_corr_matrix_wildcherry <- cor(Alkaloids_LSmeans_sums_corr_wildcherry[,3:ncol(Alkaloids_LSmeans_sums_corr_wildcherry)], 
                                  method = "pearson")

kable(Alkaloids_sums_corr_matrix_wildcherry)

# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_wildcherry <- cor.mtest(Alkaloids_sums_corr_matrix_wildcherry, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_wildcherry <- sums_cor_mtest_wildcherry$p

colnames(Alkaloids_sums_corr_matrix_wildcherry) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_wildcherry) <- axis_labels_totals %>% str_wrap(width = 24)
```

Create corrplot

```{r}
corrplot_sum_wildcherry <- corrplot(Alkaloids_sums_corr_matrix_wildcherry,
         method = "circle",
         p.mat = sums_pval_matrix_wildcherry,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "Wild Cherry") 

corrplot_sum_wildcherry
```

#### S. pimpinellifolium

```{r}
Alkaloids_LSmeans_sums_corr_pimp <- Alkaloids_LSmeans_sums_corr %>%
  filter(Class == "S. pimpinellifolium")

head(Alkaloids_LSmeans_sums_corr_pimp)

Alkaloids_sums_corr_matrix_pimp <- 
  cor(Alkaloids_LSmeans_sums_corr_pimp[,3:ncol(Alkaloids_LSmeans_sums_corr_pimp)], 
      method = "pearson")

kable(Alkaloids_sums_corr_matrix_pimp)

# run cor.mtest to get pvalues and CI for each pair of inputs
sums_cor_mtest_pimp <- cor.mtest(Alkaloids_sums_corr_matrix_pimp, 
                            conf.level = 0.95)

# save the pvalue matrix
sums_pval_matrix_pimp <- sums_cor_mtest_pimp$p

colnames(Alkaloids_sums_corr_matrix_pimp) <- axis_labels_totals %>% str_wrap(width = 24)
rownames(Alkaloids_sums_corr_matrix_pimp) <- axis_labels_totals %>% str_wrap(width = 24)
```

Create corrplot

```{r}
corrplot_sum_pimp <- corrplot(Alkaloids_sums_corr_matrix_pimp,
         method = "circle",
         p.mat = sums_pval_matrix_pimp,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 1, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 1, # size of axis text
         tl.col= "black", # color of axis text
         title = "S. pimpinellifolium") 

corrplot_sum_pimp
```

### On all isomers

```{r}
Alkaloids_allisomers_corr_matrix <- cor(Alkaloids_LSmeans_allisomers_corr[,3:ncol(Alkaloids_LSmeans_allisomers_corr)], 
                                  method = "pearson")

kable(Alkaloids_allisomers_corr_matrix)

# run cor.mtest to get pvalues and CI for each pair of inputs
allisomers_cor_mtest <- cor.mtest(Alkaloids_allisomers_corr_matrix, 
                            conf.level = 0.95)

# save the pvalue matrix
allisomers_pval_matrix <- allisomers_cor_mtest$p
```

Prettying up the axis labels

```{r}
axis_labels_allisomers <- c("Dehydrotomatidine",
                 "Tomatidine",
                 "Dehydrotomatine 1",
                 "Dehydrotomatidine 2",
                 "Total Dehydrotomatidine",
                 "Alpha-Tomatine",
                 "Hydroxytomatidine 1",
                 "Hydroxytomatidine 2",
                 "Hydroxytomatidine 3",
                 "Hydroxytomatidine 4",
                 "Total Hydroxytomatidine",
                 "Acetoxytomatidine 1",
                 "Acetoxytomatidine 2",
                 "Acetoxytomatidine 3",
                 "Total Acetoxytomatidine",
                 "Dehydrolycoperoside F/G/Dehydroesculeoside A",
                 "Lycoperoside F/G/Esculeoside A 1",
                 "Lycoperoside F/G/Esculeoside A 2",
                 "Total Lycoperoside F/G/Esculeoside A",
                 "Escueloside B 1",
                 "Escueloside B 2",
                 "Escueloside B 3",
                 "Total Escueloside B",
                 "Total SteroidalAlkaloids")

colnames(Alkaloids_allisomers_corr_matrix) <- axis_labels_allisomers %>% str_wrap(width = 24)
rownames(Alkaloids_allisomers_corr_matrix) <- axis_labels_allisomers %>% str_wrap(width = 24)
```

Create corrplot

```{r}
corrplot_allisomers_fullpop <- corrplot(Alkaloids_allisomers_corr_matrix,
         method = "circle",
         p.mat = allisomers_pval_matrix,
         insig = "label_sig", 
         type = "upper",
         sig.level = c(.001, .01, .05),
         outline = TRUE,
         pch.cex = 0.5, # size of asterisks
         pch.col = "white", # color of asterisks
         tl.cex= 0.5, # size of axis text
         tl.col="black", # color of axis text
         title = "Full Population") 

corrplot_allisomers_fullpop
```
